Aerial image restoration is an important low-level vision task for improving the quality and reliability of remote sensing and aerospace imagery. Compared with general natural image restoration， aerial image restoration faces more complex imaging conditions caused by several factors， including long-distance observation， platform motion， atmospheric interference， illumination variation， and sensor limitations. However， these factors often lead to diverse and coupled degradations， which not only reduce visual quality but also affect downstream tasks， such as object detection， semantic segmentation， change detection （CD）， scene understanding， and autonomous perception. Therefore， robust aerial image restoration has become a key problem in both computer vision （CV） and aerospace information processing. In recent years， image restoration has evolved from task-specific models for individual degradations to unified restoration frameworks and large model-driven paradigms. To date， existing surveys mainly focus on general natural image restoration， specific low-level vision tasks， or individual degradation problems. In contrast， the present survey focuses on aerial image restoration from the perspective of large model empowerment. It also emphasizes the unique imaging characteristics and practical requirements of aerial scenarios， as well as systematically reviews the technical evolution from single-degradation restoration to unified modeling， and further to large model-driven intelligent restoration. In particular， this paper highlights the contributions of large-scale pretraining， multimodal representation learning， physical priors， and agent-based decision mechanisms to robust aerial image restoration under complex degradation conditions. First， this paper reviews representative restoration methods for typical single-degradation tasks and summarizes their basic modeling ideas， advantages， and limitations. Traditional methods usually rely on handcrafted priors， optimization models， and physical assumptions， all of which provide clear interpretability but are often limited in complex real-world scenarios. While deep learning-based methods improve restoration performance by learning nonlinear mappings from degraded images to clean images， many of them are still designed for specific degradation types and may lack generalization ability when facing unknown or mixed degradations in aerial scenes. To solve this problem， this paper discusses unified image restoration methods for complex aerial imaging scenarios. In practical applications， aerial image degradation is rarely caused by a single factor. Different degradation factors may coexist and interact with each other， resulting in complex coupled degradation and spatially non-uniform degradation distributions. As such， unified restoration methods aim to handle multiple degradations within a single framework. To date， existing studies have mainly explored multidegradation modeling， prompt-based restoration， and dynamic routing or expert-based frameworks. These methods improve the adaptability of restoration models by learning shared representations， introducing degradation-aware prompts， or dynamically selecting suitable restoration paths. However， challenges remain in terms of explicitly modeling degradation mechanisms， distinguishing different degradation patterns， and dealing with spatially varying degradations. On this basis， this survey focuses on large model-driven aerial image restoration. Vision foundation models provide transferable visual representations and structural priors through large-scale pretraining， which can improve restoration robustness under data-limited and cross-scene conditions. Multimodal large models introduce semantic and cross-modal information into the restoration process， thereby enabling models to better understand complex scenes and provide high-level guidance for structure recovery. The combination of physical models and large models provides a promising hybrid paradigm in which physical priors derived from imaging mechanisms constrain the restoration process， while large models provide strong representation and generation capabilities. Meanwhile， agent-based restoration methods extend image restoration from a static mapping process to a dynamic decision-making process， in which degradation analysis， strategy planning， model selection， feedback evaluation， and iterative optimization can be integrated into a unified restoration pipeline. Furthermore， this paper analyzes the main challenges faced by existing aerial image restoration methods. First， the modeling of coupled degradations remains insufficient， especially when multiple degradation factors interact in complex imaging environments. Second， existing collaborations between semantic priors and low-level visual features remain limited， making it difficult to achieve reliable structure recovery under severe degradation. Third， the integration of physical knowledge and data-driven large models remains underexplored， and balancing physical consistency with generative flexibility is still an open problem. Fourth， most existing methods lack dynamic adaptive mechanisms and are unable to adjust restoration strategies in accordance with image content， degradation state， or task requirements. Finally， the high computational cost of large model-driven methods restricts their deployment on airborne platforms， edge devices， and real-time remote sensing systems. Overall， aerial image restoration is moving from degradation-specific and pixel-level processing toward unified modeling， multimodal collaboration， physical prior-guided restoration， and intelligent decision-making. In summary， this survey aims to provide a systematic reference for researchers by clarifying the relationship between traditional restoration methods， unified restoration frameworks， and large model-driven paradigms. It also discusses future research directions， including multisource data construction， unified representation learning， multimodal and physical prior-guided restoration， adaptive decision-making， and efficient deployment， all of which are driven by the goal of promoting robust， interpretable， and intelligent aerial image restoration systems.

Airborne spectral imaging， including hyperspectral imaging （HSI） and multispectral imaging （MSI） data from unmanned aerial vehicles （UAVs） and manned aircraft， has become a cornerstone of modern remote sensing. Compared to satellites， airborne platforms offer ultra-high spatial resolution， operational flexibility， short revisit cycles， and customizable sensors. Because of these advantages， they have become indispensable for precision agriculture， environmental monitoring， military reconnaissance， urban planning， geological exploration， and cultural heritage preservation. However， airborne spectral data present unique challenges， including high dimensionality （especially for HSI with hundreds of bands）， strong spectral-spatial correlations， complex noise， geometric distortions from platform motion， variable illumination， and the mixed-pixel problem. Traditional methods （e.g.， SVM， random forests， and physical models） struggle to fully exploit these data due to limited feature representation， the curse of dimensionality， and poor generalization across sensors or flight conditions. As such， this paper systematically reviews intelligent processing techniques for airborne spectral images， focusing on deep learning and advanced machine learning. First， we highlight the complementary roles of HSI and MSI， stating that HSI provides rich continuous spectra for fine material identification and sub-pixel analysis， while MSI offers higher signal-to-noise ratio， lower data volume， and better real-time performance. Their synergistic use， often in a layered strategy （MSI-based coarse detection followed by HSI-based fine identification）， is a key trend. Next， we discuss in detail the preprocessing pipeline—radiometric calibration， atmospheric correction， geometric registration， denoising， and resolution enhancement—noting the shift from purely physical models to hybrid data-driven and physics-informed deep learning. The core of the review covers six intelligent processing tasks. For spectral-spatial feature extraction， we trace the evolution from 2D/3D CNNs and hybrid CNN-Transformer models to state-space models like Mamba， which have been shown to achieve linear complexity while capturing long-range spectral dependencies， balancing accuracy and efficiency for real-time scenarios. For classification and segmentation， we discuss supervised， semisupervised， few-shot， and self-supervised methods （e.g.， HybridSN， Spectral-Former， and masked spectral modeling）， as well as lightweight networks （MobileNet and EfficientNet） and transfer learning dominate for MSI， with cross-domain few-shot learning addressing label scarcity. For target detection， HSI exploits subtle spectral differences for camouflaged targets and chemical agents using anomaly detection or Transformer-based detectors （e.g.， SpecDETR）， while MSI leverages YOLO and Faster R-CNN for real-time object detection. In addition， multimodal fusion （visible， thermal， spectral） further improves robustness. For change detection （CD）， Siamese networks， Transformers （BIT and ChangeFormer）， and Mamba hybrids are applied to multitemporal images， overcoming specific challenges in distinguishing real changes from illumination/atmospheric variations and incorporating physical constraints. For spectral unmixing， autoencoders learn nonlinear mixtures but lack physical constraints （nonnegativity， sum-to-one， and energy conservation）， with recent efforts embedding linear mixture or radiative transfer models into deep networks for better interpretability. For multisource fusion， we cover heterogeneous fusion （HIS + LiDAR and HIS + SAR） using multimodal Transformers or GNNs， as well as spectral-spectral fusion （HIS + MSI） to generate high-resolution hyperspectral data， outlining four collaborative strategies： HSI-guided band selection， MSI-assisted dimensionality reduction， layered cascaded processing， and end-to-end joint modeling. A dedicated section addresses real-time processing and lightweight deployment on airborne edge devices （e.g.， NVIDIA Jetson）. Furthermore， we review model compression （pruning， quantization， knowledge distillation， and neural architecture search） and evaluate backbones （MobileNet， HybridSN， Mamba， and efficient Transformers） for onboard inference. While real-time HSI classification and MSI detection have been demonstrated， real-time unmixing and CD remain challenging. The paper surveys typical applications， emphasizing HSI/MSI complementarity， including precision agriculture （early disease detection， nutrient/water stress， and fruit counting）， environmental and disaster monitoring （oil spills， red tides， forest fires， air pollution， and mining）， military reconnaissance （camouflage target detection and chemical agents）， urban and infrastructure （land cover classification， building CD， traffic， and infrastructure health）， geological exploration （mineral mapping， alteration zones， and oil/gas microseepage）， and archaeology （subsurface relics， surface disease analysis）. Next， we identify five core challenges， namely scarcity of high-quality labeled data， insufficient model generalization， real-time constraints， and lack of interpretability （black-box models ignoring physical mechanisms）， and difficulties in air-space-ground collaboration （heterogeneous data， limited bandwidth， and cross-platform transfer）. Finally， we outline the following directions for future works： foundation models with self-supervised learning for spectral-spatial representation； lightweight edge intelligence via hardware-software co-design； physics-informed deep models embedding radiative transfer or mixture constraints； robust and trustworthy AI with adversarial defense and uncertainty quantification； and integrated air-space-ground collaborative observation systems for hierarchical and intelligent remote sensing. In summary， this review provides a systematic reference for researchers and engineers advancing airborne spectral image intelligent processing. The datasets mentioned in this paper have been compiled and are available at https://github.com/zhaoxb2025/Airb-spe-Img.

Cross-domain few-shot object detection （CD-FSOD） in aerial remote sensing aims to detect target objects when the target domain contains only a few annotated instances and differs substantially from the source domain. This setting is important for various applications （e.g.， land resource investigation， disaster monitoring， traffic supervision， maritime and airport surveillance， emergency response， and military reconnaissance）， because many operational remote sensing systems must be deployed in new regions， seasons， sensors， or platforms before large-scale annotation is available. Typically， conventional object detectors assume abundant labeled data and a relatively stable training-test distribution. In contrast， aerial images are influenced by platform altitude， satellite or unmanned aerial vehicle （UAV） imaging geometry， optical or synthetic aperture radar （SAR） sensing mechanisms， spatial resolution， illumination， weather， seasonal background， geographical scene composition， and category occurrence frequency. These factors change target scale， texture， orientation， density， and contextual appearance at the same time. Consequently， detectors trained on a well-annotated source domain may suffer from severe classification confusion， localization drift， false alarms， and missed detections after being transferred to a new target domain. Related to this， the current paper provides a systematic survey of CD-FSOD for aerial remote sensing scenes. First， the task definition and research boundary are clarified. In particular， CD-FSOD is distinguished from classic object detection， few-shot object detection （FSOD）， cross-domain object detection， and general remote sensing object detection. Classic object detection emphasizes classification and localization under roughly independent and identically distributed data. FSOD focuses on new-category detection with limited examples but often assumes that the base and novel domains are close. Meanwhile， cross-domain object detection addresses domain shift but usually does not impose an extreme target-domain annotation constraint. Finally， remote sensing object detection describes the application scenario and its characteristic difficulties， including small objects， dense object distributions， arbitrary orientations， and complex backgrounds. CD-FSOD integrates the last two difficulties in which the target domain is both data-scarce and distributionally shifted. Second， existing methods are organized into five technical routes. Transfer and domain-alignment methods （i.e.， domain-adaptive faster region-based convolutional neural network （DA Faster R-CNN）， strong-weak distribution alignment （SWDA）， spatial attention pyramid network （SAPNet）， and adaptive teacher frameworks） reduce the discrepancy between source and target features by image-level， feature-level， instance-level， or teacher-student constraints. Meta-learning and metric-learning methods， including meta region-based convolutional neural network （Meta R-CNN）， attention region proposal network （Attention-RPN）， few-shot object detection via contrastive proposal encoding （FSCE）， Detect everything with vision Transformer （DE-ViT）， and CD-ViTO， all improve the use of support samples through episodic learning， class prototypes， relation modeling， and contrastive representation learning. Meanwhile， generative augmentation methods， such as hallucination-based feature synthesis， multiperspective data augmentation （MPAD）， AeroGen， Control Copy-Paste， and Domain-RAG， expand the target-domain sample distribution by generating features， foreground instances， target layouts， or domain-consistent backgrounds. To compensate for insufficient visual examples， vision-language alignment methods， including RegionCLIP， PromptDet， Grounding DINO， and rich-text cross-domain multimodal FSOD， introduce category names， attributes， scene descriptions， and professional textual semantics. Furthermore， large-model methods exploit visual foundation models， detection transformer （DETR） variants， and multimodal large models （e.g.， Rex-Omni） to provide broader open-vocabulary recognition and stronger initial representations. Third， the paper summarizes datasets， task protocols， and evaluation metrics. The reviewed benchmarks include multidomain few-shot object detection （MoFSOD）， CD-FSOD， object detection in optical remote sensing images （DIOR）， dataset for object detection in aerial images （DOTA）， xView， vehicle detection in aerial imagery （VEDAI）， and the Northwestern Polytechnical University very-high-resolution 10-class dataset （NWPU VHR-10）. Their differences can be detected in terms of image number， category number， bounding-box type， scene coverage， object scale， and whether the protocol stresses a balanced K-shot instance setting. Common metrics include precision （P）， recall （R）， average precision （AP）， mean average precision （mAP）， AP50， AP75， mAP@50：95， novel-class mAP， and intersection over union （IoU）. Instead of listing formulae for routine metrics， this survey discusses their specific meanings and how they should be used to evaluate target-domain generalization， shot sensitivity， robustness across domains， and localization quality for small dense objects. Fourth， this review identifies several open problems. Notably， complex domain shift should be modeled at global-scene， local-object， and category-conditional levels rather than by a single overall feature alignment. Few-shot robustness requires support-sample quality assessment， stable prototype learning， multiscale representation， uncertainty estimation， and joint optimization of classification and localization. Current datasets and evaluation protocols remain partly disconnected from real remote sensing applications， in which categories are long-tailed， annotations are incomplete， sensors are heterogeneous， and deployment constraints are strict. To date， multisource adaptations across optical， infrared， multispectral， and SAR data remain underexplored. While foundation models and multimodal large models offer stronger semantic priors， they also introduce problems， such as high computational cost， insufficient remote sensing domain knowledge， and limited performance on small， dense， rotated， or sensor-specific targets. Therefore， to improve reuse and citation of the survey materials， a curated performance summary of representative algorithms under the aerial remote sensing CD-FSOD setting is provided in the manuscript. Overall， CD-FSOD in aerial remote sensing is moving from isolated fine-tuning or domain-alignment strategies toward integrated frameworks that combine domain adaptation， few-shot discrimination， data generation， vision-language semantics， and foundation-model transfer. In terms of practical use， it is emphasized that CD-FSOD should not be evaluated only by a single accuracy score obtained under a fixed split. Instead， a more informative protocol should report the stability of each method under different shots， target domains， object scales， and sensor conditions. This is because an algorithm that performs well on one aerial dataset may fail when the dominant background， ground sampling distance， or object density changes. Therefore， this survey recommends that future benchmark construction record the source of domain shift explicitly and separate the effects of category novelty， image style， spatial resolution， and annotation scarcity. Such analysis is especially necessary for aerial remote sensing， in which missed small targets and inaccurate localization may be more damaging than a modest decrease in overall mAP. Finally， future progress depends on remote-sensing-specific pretraining resources， reliable benchmark construction， efficient adaptation for high-resolution imagery， and stronger integration between precise detectors and general-purpose foundation models. This is maintained through the open resource page found in https://github.com/Farenweh/CD-FSOD-Links(Github) or https://gitee.com/cby1241385936/cd-fsod-links(Gitee).

The rational and effective use of multisource information tend to expand the spatial and temporal coverage of measurements， fully excavate the target feature information contained in various sensors， greatly reduce the ambiguity of information， and improve detection performance. Faced with multimodal target data from different types， characteristics， and perception methods in some applications， such as in airborne multisensor target-tracking systems， various types of sensors with different working attributes （e.g.， visible light images， infrared images， optical sensors， microwave radar， and LiDAR） are often involved， presenting measurement information of the target from different perspectives. The point cloud information obtained by LiDAR not only achieve high-precision ranging but also provide more accurate spatial information due to the varying reflectivities of different objects. However， this is susceptible to noise generated by complex environments and lacks semantic information. Compared to point cloud information， target image information provides rich surface textures and contextual semantic information that can more accurately restore the appearance and structure of objects； however， it often lacks in-depth information. The current landscape is marked by the rapid development of sensor networks， information perception methods， and big data processing technologies. Faced with information perception from different types， characteristics， and methods， multimodal information fusion technology has received increasing attention. This is due to its advantages of human-like automatic perception and powerful comprehensive reasoning capabilities； its multidomain and multimodal nature， as well as its ambiguity， and incomplete data association. The paper first introduces the significance and necessity of multimodal information fusion. Furthermore， at the fusion architecture level， it outlines the evolution from early fusion， late fusion， to hybrid fusion， revealing the trade-off relationship between information retention and computational efficiency in each architecture. At the same time， this paper elaborates on three cutting-edge multimodal fusion methods： attention-based interaction modeling， semantic alignment based on contrastive learning， and generative fusion with LLMs as the carrier. Then， it introduces typical multimodal datasets， such as the COCO dataset， LAION-400M dataset， Visual Genome dataset， MS COCO Captions dataset， Conceptual Captions dataset， Flickr30k dataset， and aviation-related datasets， along with their corresponding application fields. Moreover， the paper further explores the typical military applications of multimodal fusion， including battlefield situation awareness， multisource intelligence analysis， and unmanned system collaboration. This work also emphasizes that high-quality single-modal representations are no longer a bottleneck with the maturity of contrastive learning and multimodal pre-training. To date， the research focus has shifted from how to map heterogeneous modalities to a unified space in the early stage to how to design interaction mechanisms that can capture complex， dynamic， and even contradictory relationships between modalities based on representation alignment. On the basis of fusion architecture， fusion models， and computational costs， we propose three development directions for multimodal information fusion. We argue that the choice of multimodal fusion architecture directly impacts the degree of information retention， computational efficiency， and model interpretability. Thus， existing methods can be categorized into early fusion， late fusion， hybrid fusion based on the stage at which information fusion occurs. Early fusion， also known as data-level fusion， involves the integration of multisource information prior to or at the shallowest level of modality-specific feature extraction. This process preserves the richest original information and multimodal interaction details. Typical implementations include multimodal concatenation and multi-view encoding. Taking the visual-language model as an example， early fusion concatenates image patch embeddings and text word embeddings into a unified sequence， which is then fed into a joint encoder for processing. The advantage of this architecture lies in its ability to establish multimodal associations at a shallow level， thus facilitating the capture of fine-grained modality interactions. However， early fusion faces a severe dimensionality catastrophe problem. When the number of modalities increases or the feature dimensions of each modality become extremely high， the joint representation space grows exponentially. At the same time， during early fusion， the noise of some modalities may be amplified due to the varying noise characteristics of different modalities， resulting in poor representation quality. Meanwhile， late fusion adopts a diametrically opposite strategy in which each modality independently performs feature extraction and task prediction， with integration， weighted average， and meta-learner only at the final decision-making layer. In this “divide and conquer” design， the modal branches can be trained in parallel， achieving high computational efficiency； meanwhile， overfitting or noise in one modality is less likely to affect other modalities. Additionally， in scenarios wherein some modalities are missing， the remaining branches can still work normally， demonstrating strong system robustness. However， late fusion may lead to a higher consumption of computational resources as it requires training independent models for each modality. At the same time， independent models for each modality struggle to capture low-level interactions between them， making it difficult to model simple fusion at the decision-making level. Furthermore， data from different modalities may face alignment issues in time or space， such as the synchronization of video frames and audio signals. Hybrid fusion introduces cross-modal interaction in the middle layers of the network while maintaining modality-specific processing paths， aiming to combine the strengths of both. Its typical application is to use modality-independent encoders at the bottom layer， introduce a cross-attention module in the middle layer to achieve feature-level interaction， and separate again at the top layer to preserve modality-specific information. A deeper evolutionary direction is dynamic fusion， which allows the network to autonomously decide where and with what intensity information from various modalities can be fused. For example， a gated mechanism-based visual image and LiDAR multimodal fusion network dynamically adjusts the weights of information from each modality based on input data. Specifically， it places more trust in visual images under strong lighting conditions， while giving higher weight to LiDAR point cloud information under low-light conditions. The choice of fusion architecture does not have an “optimal solution”； rather， it depends on the function of task characteristics and resource constraints. Generally， when there are fine-grained， location-related interactions between modalities （e.g.， image-text alignment）， early fusion is better. When each modality can complete predictions independently and some modalities are prone to missing， late fusion is more robust. Meanwhile， dynamic fusion strikes a good balance between performance and robustness. Looking back at the development of multimodal fusion， the bottleneck of fusion has shifted from “representation” to “interaction”. With the maturity of contrastive learning and multimodal pre-training， high-quality single-modality representation is no longer a bottleneck. In particular， the research focus has shifted from how to map heterogeneous modalities into a unified space in the early stage to how to design interaction mechanisms that can capture complex， dynamic， and even contradictory relationships between modalities based on representation alignment. Furthermore， fusion architecture has evolved from “static design” to “dynamic adaptation”. In the real world， the correlation and reliability of modalities change dynamically with the environment， and the limitations of fixed fusion strategies are becoming increasingly evident. In military confrontations， electronic jamming may render specific sensors inoperative. Therefore， to enhance the robustness of the fusion system the activation state of modalities and fusion weights must be dynamically adjusted on the basis of input content and task context. The causal fusion model holds promise for breaking through the current limitations of relational learning. At present， most existing methods focus on learning statistical correlations between modalities， rather than causal relationships. This leads to two issues. First， the model is prone to learning spurious correlations； second， it struggles to generalize to environments beyond the training distribution. Therefore， introducing causal inference tools can enhance the fusion model’s adversarial robustness and environmental transferability. Furthermore， a new multimodal fusion mechanism is designed in this paper. To date， redundant information from each modality may cause computational waste， and the corresponding noise information may affect fusion performance. Thus， how to utilize information theory techniques to identify and retain complementary information while suppressing redundancy and noise is a new development direction for multimodal fusion technology.

Semantic segmentation is a fundamental task in computer vision that aims to assign a semantic label to each pixel in an image. Most conventional semantic segmentation methods are designed for a closed-set setting in which the categories involved in testing are predefined and largely consistent with those observed during training. Although this setting facilitates the learning of stable category representations and clear decision boundaries， it limits the applicability of segmentation models in real-world scenarios， where unseen objects and novel semantic concepts are frequently encountered. In this context， open-vocabulary semantic segmentation （OVSS） has emerged as an important research direction for extending semantic segmentation from closed-set prediction to open-world visual understanding. OVSS enables models to identify and segment pixel-level regions corresponding to arbitrary textual concepts by using natural language descriptions as category definitions， thereby alleviating the dependence on fixed label spaces. The rapid development of vision-language pre-trained models， especially contrastive language-image pre-training （CLIP） and related large-scale cross-modal models， has provided an important foundation for OVSS. Trained on massive image-text pairs， these models learn aligned visual and textual representations and exhibit strong transferability in open-category recognition. However， most existing vision-language models are mainly optimized for image-level semantic alignment and global visual recognition. Thus， these models have difficulty meeting the requirements of pixel-level dense prediction directly， such as accurate localization， boundary delineation， and fine-grained category discrimination. Therefore， how to effectively transfer image-level open-vocabulary recognition ability to pixel-level segmentation remains a key challenge in OVSS. This paper summarizes recent progress in OVSS from the perspectives of task background， representative methods， benchmark datasets， evaluation metrics， remote sensing extension， and future directions. First， the paper introduces the task background and basic characteristics of OVSS， after which its relationship with conventional semantic segmentation and zero-shot semantic segmentation is clarified. Compared with conventional semantic segmentation， OVSS emphasizes generalization beyond predefined categories， whereas when compared with zero-shot semantic segmentation， it further benefits from large-scale image-text pre-training and allows more flexible category definitions through natural language descriptions. Second， representative OVSS methods are reviewed in accordance with several major research routes， including zero-shot semantic segmentation， early explorations based on image-text supervision， two-stage methods， and single-stage methods. Early zero-shot segmentation methods mainly rely on semantic embeddings to transfer knowledge from seen categories to unseen categories， but their expressive ability is often limited by static semantic representations. Meanwhile， initial studies based on image-text supervision explore whether transferable semantic region representations can be learned without dense pixel annotations， thus providing important inspiration for subsequent OVSS methods. Two-stage methods usually decompose the task into class-agnostic region generation and open-vocabulary region recognition. While this paradigm is clear and modular， its performance is highly dependent on the quality of candidate regions and may incur additional computational cost. In contrast， single-stage methods aim to integrate region modeling， vision-language alignment， and pixel-level prediction within a unified framework， thereby reducing cross-stage error accumulation and improving inference efficiency. Nevertheless， they still face challenges in fine-grained localization， dense semantic alignment， and robust generalization to unseen categories. Apart from analyzing the main ideas， technical characteristics， advantages， and limitations of these methods， this paper discusses the extension of OVSS to remote sensing imagery. Compared with natural images， remote sensing images typically exhibit top-down perspectives， large variations in object scale， significant orientation changes， complex backgrounds， and cross-region distribution shifts， all of which make it difficult to directly transfer OVSS methods designed for natural-image scenarios to remote sensing applications. Therefore， recent studies have begun to explore open-vocabulary remote sensing segmentation frameworks， dedicated benchmark datasets， training-free strategies， and multimodal fusion methods. This development indicates that OVSS is gradually expanding from general natural-image understanding to more complex specialized visual domains. Commonly used datasets and evaluation metrics in OVSS are also summarized in this paper. Existing studies typically use COCO-Stuff as the main training benchmark and evaluate model generalization on datasets， such as Pascal VOC， Pascal Context， and ADE20K， under different vocabulary settings. At present， OVSS evaluation relies on conventional semantic segmentation metrics， especially mean intersection over union （mIoU）. However， such metrics emphasize strict category matching and are insufficient for measuring semantic similarity， category hierarchy， and open-category generalization. Therefore， constructing evaluation protocols that better reflect the semantic openness of OVSS remains an important issue. Finally， the paper summarizes the major challenges in current OVSS research and discusses promising future directions， including the effective transfer of open-vocabulary recognition capabilities to precise pixel-level prediction， adaptive region generation， evaluation protocols tailored to open-vocabulary settings， learning with limited supervision， training-free inference， and extensions to domain-specific applications.

With the rapid expansion of the low-altitude economy， accompanied by the increasing maturity of intelligent unmanned systems， unmanned aerial vehicles （UAVs） are gradually transforming from traditional remotely controlled flying platforms into autonomous aerial agents that are fully capable of perception， reasoning， and decision-making. Among the various sensing modalities available to UAVs， visual perception plays a central role in acquiring environmental information and enabling high-level situational awareness. Hence， visual understanding capability can directly determine the intelligence level and operational autonomy of UAV systems. In recent years， the emergence of visual foundation models， vision-language models， and multimodal large language models （LLMs） has significantly influenced the technical paradigm of UAV visual understanding. As a result， this paradigm shift has provided new opportunities for enabling UAV systems to operate effectively in complex and open environments in which perception， reasoning， and decision making must be tightly coupled. To clarify this emerging research landscape， this paper introduces a capability-oriented analytical framework that organizes UAV visual understanding into three hierarchical levels： basic perception， semantic reasoning， and decision planning. This framework serves as the conceptual backbone of the survey and allows recent studies to be examined from a systematic capability-evolution standpoint. Specifically， from the task perspective， we construct a comprehensive taxonomy of UAV visual understanding tasks that includes four major categories： 1） basic object perception， 2） event semantic analysis， 3） spatial environment understanding， and 4） flight decision-making. Within this taxonomy， representative tasks （i.e.， object detection， target tracking， human action recognition， visual question answering， spatial reasoning， navigation， and autonomous flight control） are analyzed in a unified manner. At the same time， this paper summarizes several fundamental challenges that arise in aerial visual perception. These challenges include significant scale variations caused by high-altitude viewpoints， long-range observations that lead to small object representations， complex backgrounds with strong visual clutter， and dynamic environmental changes that require robust temporal reasoning. From the technical perspective， this paper reviews the methodological evolution that underlies the development of UAV visual understanding models. Early approaches were largely based on conventional deep learning architectures that focused on supervised visual perception tasks. Then， subsequent advances in visual foundation models significantly improved representation learning by leveraging large-scale pretraining and open-vocabulary multimodal alignment. More recently， LLMs and multimodal LLMs have introduced powerful reasoning capabilities and cross-modal interaction mechanisms that allow UAV systems to interpret visual observations along with natural language instructions and contextual knowledge. Building upon these developments， embodied vision-language-action models have begun to connect perceptions with action generation， thus enabling UAVs to perform complex task planning and interactive decision making in real-world environments. From the capability perspective， this paper further examines how large visual understanding models enhance UAV intelligence across three dimensions： 1） visual perception enhancement， where large models improve robustness in open-vocabulary recognition， small-object detection， and fine-grained visual understanding； 2） vision-language reasoning， where multimodal models facilitate complex reasoning processes such as spatial relation reasoning， event interpretation， and cross-modal knowledge integration； and 3） visual decision planning， where embodied multimodal models enable UAVs to translate perception and reasoning outcomes into actionable flight strategies， mission planning procedures， and adaptive control policies. Additionally， representative UAV visual datasets and benchmarks that support the development and assessment of large multimodal models for visual understanding are summarized in this work. Particular attention is given to the evolution of evaluation protocols. Traditional benchmarks often measure performance in narrowly defined tasks， such as detection or classification. However， recent research has increasingly emphasized capability-oriented evaluation frameworks that assess broader competencies， namely reasoning ability， cross-task generalization， and decision support. This transition reflects a broader shift in the field toward evaluating integrated visual intelligence rather than isolated perception performance. Finally， this work discusses several promising research directions that may shape the future development of UAV visual understanding systems. These include the construction of general-purpose visual foundation models tailored for aerial scenarios， the advancement of embodied UAV intelligence through vision-language-action integration， the development of real-time reasoning techniques and lightweight deployment strategies suitable for resource-constrained aerial platforms， and the establishment of safety-aware， trustworthy， and privacy-preserving UAV perception systems.

Unmanned aerial vehicle （UAV） vision-language navigation stands as a core research direction of aerial embodied intelligence， which combines computer vision （CV）， natural language processing， robot control， and unmanned system technology. This system endows UAVs with the ability to understand human natural language instructions and perform autonomous navigation， path planning and task execution in unstructured， GPS-denied and dynamically changing three-dimensional environments. Unlike traditional UAV navigation methods that rely on satellite positioning， inertial navigation or manual waypoint setting， UAV vision-language navigation establishes a direct mapping from high-level semantic instructions to low-level continuous flight actions. This enables UAVs to complete complex tasks， such as target search， obstacle avoidance， long-distance cruising， and scene reasoning only through visual observation and language understanding. This technology effectively breaks the limitations of traditional navigation in semantic interaction and environmental adaptability. At the same time， it has theoretical value and engineering application prospects in low-altitude economies， urban security， emergency rescue， industrial inspection， and precision agriculture. In recent years， with the rapid development of multimodal large models， Transformer architectures and embodied artificial intelligence （AI）， UAV vision-language navigation has gradually evolved from early modular pipelines to end-to-end learning frameworks， and then to highly interpretable reasoning systems driven by large language models （LLMs） and vision-language models. However， compared with ground robot vision-language navigation， UAV vision-language navigation still faces many unique challenges caused by aerial movement characteristics. First， UAVs move with six degrees of freedom， and frequent changes in height， pitch， yaw， and roll lead to unstable visual input， large differences in object scales and serious geometric distortion， which in turn increases the difficulty of cross-modal alignment between vision and language. Second， three-dimensional spatial topology is more complex， and spatial prepositions， such as “above”， “between”， “along”， and “across” require stronger geometric reasoning and spatial awareness， which cannot be satisfied by two-dimensional image matching alone. Third， long-range navigation tasks bring certain problems， such as massive visual information， cumulative positioning errors， and semantic forgetting， which put forward higher requirements for efficient memory mechanism and environmental representation. Fourth， the discrete semantic decision space is difficult to match with the continuous physical control spaces of UAVs， resulting in a large gap between simulation training and real-world deployment. With the goal of solving the abovementioned problems， this paper systematically summarizes the research progress of UAV vision-language navigation from the perspective of embodied intelligence. First， three types of simulation platforms are reviewed： general robot simulation platforms， simulation platforms based on real scene reconstruction， and large-scale virtual simulation platforms built by game engines. These platforms provide safe， low-cost， and high-efficiency test environments for algorithm verification， data generation， and model training， as well as effectively narrow the domain gap between simulation and reality. Then， mainstream datasets are compared and analyzed from the dimensions of scene scale， instruction complexity， action space definition， and environmental authenticity. The development of datasets reflects the trend of UAV vision-language navigation moving from indoor structured scenes to outdoor large-scale urban scenes， from short and simple to long sequential reasoning instructions， and from discrete actions to continuous six-degree-of-freedom control. Next， the core technical framework is divided into four modules： perception representation， reasoning paradigm， memory storage and embodied control. The perception representation focuses on visual feature extraction， geometric alignment， multimodal fusion， and world model construction. The reasoning paradigm mainly includes cross-modal attention mechanism， transformer-based pretraining， and large-model-driven chain-of-thought reasoning. The memory storage evolves from implicit temporal memory to explicit semantic maps and topological graphs. Furthermore， the embodied control develops from modular trajectory planning to end-to-end vision-language-action generation with safety constraints. In terms of practical applications， UAV vision-language navigation has been widely explored in urban infrastructure inspection， disaster emergency search and rescue， intelligent logistics distribution， and precision agricultural monitoring. This can reduce manual participation， improve operation efficiency， and enhance safety under high-risk， high-complexity and high-efficiency-demanding conditions. However， key bottlenecks continue to restrict large-scale deployment， such as simulation-to-real transfer， generalization in unseen environments， real-time performance of onboard computing， safety and stability in dynamic environments， and collaborative navigation of multiple UAVs. Finally， this paper discusses the prospects and future development trends of UAV vision-language navigation， including stronger world model and predictive reasoning， better generalization and robustness based on multimodal large models， safer and more reliable physical control， more efficient human-machine interaction and collaborative intelligence， as well as deeper integration with low-altitude digital economy and unmanned system ecology. Overall， this review aims to provide a complete and clear technical route for researchers in the fields of embodied intelligence， computer vision， natural language processing， and UAV systems； promote the breakthrough of key technologies； and accelerate the practical and industrialization process of UAV vision-language navigation.

Today， the aviation industry faces a critical shortage of qualified pilots and escalating labor costs due to the continuous surge in global air transport demand. Consequently， single-pilot operation （SPO） has emerged as a strategic evolution in civil aviation to optimize crew resources and mitigate pilot fatigue， especially during long-haul flights. However， the transition from traditional multicrew cockpits to a single-pilot environment is not merely a reduction in personnel； rather， it introduces a significant “cognitive and decision-making gap” previously filled by the co-pilot. To bridge this gap， this paper proposes the concept of the “AI co-pilot” —— an intelligent system designed to establish a deep cognitive collaborative relationship with the human pilot through advanced perception， semantic understanding， and autonomous decision-making capabilities. This paper provides a systematic and comprehensive review of the core technological components enabling AI-assisted piloting. In this work， we propose a four-layer analytical framework known as “perception-cognition-interaction-decision-safety monitoring”. This structure serves as a blueprint for integrating heterogeneous AI modules into the high-integrity environment of an aircraft cockpit. First， we look into multimodal physiological and behavioral sensing technologies that are essential for the real-time monitoring of a pilot’s workload and fatigue state. Given that traditional subjective assessments are insufficient for high-dynamic flight environments， this review focuses on the integration of electroencephalogram （EEG） and functional near-infrared spectroscopy （fNIRS）. In particular， we provide a critical analysis of their trade-offs： while EEG offers superior temporal resolution capable of capturing instantaneous cognitive shifts， fNIRS demonstrates stronger robustness against motion artifacts in a vibrating cockpit. Furthermore， computer vision （CV） techniques are discussed for the nonintrusive monitoring of facial expressions， percentage of eyelid closure （PERCLOS）， and visual tunneling effects. The fusion of these modalities allows the AI Co-pilot to “read” the pilot’s state and trigger adaptive automation upon detection of cognitive overload. Second， the review explores intelligent speech interaction and semantic understanding within the complex controller-pilot data link communications （CPDLC） and radio environments. The aviation context poses unique challenges， including low signal-to-noise ratios in VHF communications and nonstandard accents. In this paper， we analyze the application of LLMs and transformer-based architectures in intent recognition and slot filling for air traffic control （ATC） instructions. By parsing complex， nested instructions into executable parameters， the AI Co-pilot functions as an “intelligent radio operator” that significantly reduces the auditory and manual workload of the single pilot. Third， we summarize intelligent route planning and dynamic conflict resolution driven by the synergy of large models and deep reinforcement learning （DRL）. Algorithms such as proximal policy optimization （PPO） and soft actor-critic （SAC） are evaluated for their efficacy in continuous action spaces and high-dimensional flight dynamics. Unlike traditional rule-based flight management systems （FMS）， AI-driven planners can explore optimal trajectories in unconstrained airspace and respond adaptively to extreme weather or system failures. Additionally， we emphasize the transition from individual agent optimization to multiagent reinforcement learning （MARL） for collaborative collision avoidance in dense urban air mobility （UAM） scenarios. A significant portion of this review is dedicated to the core barriers that hinder the commercial deployment of aviation AI. We provide an in-depth analysis of the non-interpretability of “black-box” deep learning models， which prevents human pilots from establishing calibrated trust during time-critical emergencies. Furthermore， the paradigm conflict between the nondeterministic nature of AI and traditional deterministic airworthiness standards （e.g.， DO-178C） is dissected. We also discuss the latest policy trends from EASA and the FAA， highlighting the shift toward “run-time assurance （RTA）” frameworks. Specifically， RTA acts as a safety shield， bounding the outputs of complex AI models within a predefined safe flight envelope and ultimately providing a viable certification pathway. Finally， this paper presents a perspective on future trends. We posit that the next generation of aviation AI will evolve toward a “multimodal unified architecture” in which text， speech， vision， and flight dynamics are aligned in a single latent space. We predict that this evolution will transform the AI Co-pilot from a collection of isolated subtasks into a holistic “cognitive partner”. By emphasizing transparency， bidirectional communication， and standardized certification， this review aims to provide a theoretical foundation and engineering reference for achieving high-transparency， deep human-machine collaborative intelligent cockpits in the SPO era.

With the proliferation of multisource sensors and the rapid progress of multimodal data fusion and intelligent interpretation techniques， aerial remote sensing continues to evolve from traditional single-modal perceptions toward multimodal perception and understanding. This progress demonstrates the significant potential for intelligent monitoring and decision-making in precision agriculture， urban environmental monitoring， ecological protection， and natural disaster assessment. Due to ongoing advancements in Earth observation capabilities， multisource remote sensing data， including optical imagery， synthetic aperture radar （SAR）， and hyperspectral imagery are being increasingly characterized by high spatial resolution， multitemporal coverage， and multidimensional richness. These developments， in turn， offering a solid foundation for fine-grained land-cover identification and dynamic change analysis. Nevertheless， traditional remote sensing interpretation methods， which rely on single-source data and single-task learning models， struggle to cope with the complexities of real-world scenarios in which object scales vary dramatically， semantic layers are rich and entangled， and spatiotemporal heterogeneity is strong. Because such methods cannot adequately balance holistic scene semantics with local fine-grained details， this limits their capacity for high-level semantic understanding and comprehensive decision-making. Compared with satellite remote sensing， aerial platforms that typically employ unmanned aerial vehicles （UAVs） and low-altitude aircraft offer high spatial resolution and great observational flexibility， facilitating the completion of several tasks， such as fine urban modeling， small-object recognition， and emergency monitoring. However， these advantages also introduce severe scale variations， highly complex backgrounds， and inconsistent imaging conditions， making it extremely difficult for conventional models to capture both global scene semantics and subtle local textures simultaneously. Recent advances in deep learning and artificial intelligence （AI） have transformed remote sensing interpretation from manual inspection toward automation and intelligence， achieving notable results in scene classification， object detection， and semantic segmentation. Yet single-task and single-modality paradigms remain inadequate in terms of fully exploiting complementary information in heterogeneous multisource data. As such， these paradigms fall short of supporting the high-level semantic comprehension and integrated decision-making required for complex applications. The emergence of multimodal large models provides a transformative pathway for intelligent aerial remote sensing interpretation. These models can jointly perform image understanding and linguistic reasoning by integrating visual encoders with large language models（LLMs） through instruction tuning and cross-modal alignment， thus achieving breakthroughs in visual-language fusion， cross-modal reasoning， and task-instruction-guided analysis. At the same time， within the remote sensing community， researchers have begun to construct large-scale image-text datasets to train vision-language models and employ linguistic guidance to enhance the comprehension of complex land-cover semantics， thus marking a critical shift from purely visual modeling toward semantically augmented modeling and laying the groundwork for the subsequent development of multimodal large models. A systematic review reveals that the evolution of remote sensing large models has followed a clear paradigm progression： from early approaches that relied solely on single visual modalities， through vision-language models that introduced textual semantics but are still confined to specific tasks， to the current stage in which unified multimodal large models enable cross-task collaboration and complex reasoning. Despite these promising advances， the application of multimodal large models to aerial remote sensing still faces several formidable challenges. The substantial disparities in spatiotemporal references and radiometric properties among heterogeneous data sources， such as optical， SAR， and hyperspectral imagery， render cross-modal alignment extremely difficult， often giving rise to semantic misalignment and information loss. While fine-grained spatial structures and multiscale objects inherent in aerial imagery impose stringent demands on spatial cognition and multiscale reasoning， general-purpose large models often lack sufficient domain-specific spatial priors to accurately capture geometric and structural relationships. High-resolution scenes that impose massive computational and storage burdens also make real-time inference on edge devices highly challenging， resulting in the failure to meet the stringent timeliness and generalization requirements of time-sensitive tasks， such as disaster response and UAV-based inspection. Moreover， the black-box nature of model decision-making leads to insufficient interpretability and trustworthiness that hinders deployment in safety-critical scenarios. Meanwhile， data privacy concerns have become increasingly prominent within distributed collaborative learning frameworks. In response to the abovementioned challenges， this paper systematically reviews recent advances in multimodal data fusion and large model technologies for aerial remote sensing by tracing the shift from single-source to multimodal integration. Driven by large models， remote sensing interpretation has gradually evolved from low-level perception to cross-modal reasoning and semantic understanding. Nevertheless， substantial challenges remain in multimodal alignment， spatial cognition， task reliability， and practical deployment in real-world scenarios. To help resolve these problems， we analyze cross-sensor fusion and emphasize how disparities in geometry， radiometry， and acquisition timing among optical， SAR， and LiDAR data severely impede cross-modal semantic alignment. Furthermore， despite advances in vision-language pretraining， high resolution and complex backgrounds often degrade alignment accuracy， thus requiring fine-grained multimodal representations through geometric correction， radiometric normalization， and spatiotemporal consistency modeling. For multiscale spatial reasoning， we emphasize that complex tasks require holistic understanding of structural relations and spatial distributions. We argue that while existing models partially strengthen spatial reasoning via region-based interaction and spatial question answering， a unified framework for global spatial relation modeling is needed to elevate task awareness and multilevel reasoning. Generative AI for few-shot interpretation has the potential to alleviate annotation scarcity and support time-critical missions， such as disaster response. Regarding trustworthiness and interpretability， peculiar data distributions and insufficient instruction data in remote sensing exacerbate hallucinations in large vision-language models. Thus， domain knowledge， task constraints， and dedicated evaluation systems are required to enforce reasoning consistency and reliable high-stakes decisions. For efficient edge deployment and privacy， high-resolution large-format data impose heavy computational and storage burdens. Furthermore， although lightweight networks continue to emerge， trade-offs among real-time performance， stability， and complex scene adaptability remain， which means that model compression， inference optimization， and hardware-software co-design become critical. Related to this， federated learning offers a privacy-preserving mechanism for multisource data integration. In summary， addressing these challenges is important not only for advancing the theoretical understanding of cross-modal semantic reasoning and multiscale spatial cognition but also for facilitating the practical deployment of intelligent aerial remote sensing systems in high-stakes， real-world applications.