| --- |
| license: cc-by-nc-4.0 |
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| --- |
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| ## Domains |
| | 序号 | 无人机 Domain | 领域定位 | 典型检测目标 | 典型应用 | 核心输出 / 特点 | |
| | -- | ---------------- | ---------------------------- | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------- | |
| | 1 | **农林牧业与生态监测** | 检测自然环境中的生物目标及农业生产对象 | 农作物、果实、花朵、植株<br>缺苗、倒伏作物、杂草<br>牛、羊等牲畜<br>野生动物、鸟类<br>病树、枯死树、倒伏树木<br>农业机械、作业人员 | 作物株数和果实数量统计<br>缺苗、杂草和倒伏区域发现<br>牲畜自动盘点和走失牲畜搜索<br>野生动物检测、跟踪和数量调查<br>森林病树、枯死树和风倒木识别<br>农林区域非法人员或车辆检测 | 目标类别<br>目标数量<br>目标位置<br>目标密度<br>变化趋势 | |
| | 2 | **能源、工业与基础设施巡检** | 将电力、油气、工厂、桥梁、道路等统一归入资产和设施巡检 | 输电杆塔、导线、绝缘子、金具<br>鸟巢、异物、悬挂物<br>光伏组件、热斑组件<br>风机叶片、裂纹及表面损伤<br>管道、阀门、储罐、烟囱<br>桥梁构件、道路设施、铁路设备<br>裂缝、锈蚀、破损、脱落<br>设备热点、泄漏点和异常部件 | 电力线路异物和缺陷检测<br>绝缘子、连接件和杆塔部件检测<br>光伏板热斑及故障组件定位<br>风机叶片损伤检测<br>油气管线和工业设备异常检测<br>桥梁、道路、铁路设施缺陷识别<br>设备缺失、错位或损坏检测 | **部件检测：**定位设备及零部件<br>**缺陷检测：**识别裂纹、腐蚀、破损、过热等异常 | |
| | 3 | **建筑、施工与矿山作业** | 将施工现场、露天矿、采石场和大型工程现场视为复杂作业区域 | 施工人员<br>安全帽、反光衣、安全绳等防护装备<br>挖掘机、吊车、卡车、推土机<br>建筑材料和堆料<br>临时建筑、围栏和危险区域<br>裂缝、坍塌、落石<br>非法进入人员和车辆<br>烟雾、明火和积水 | 施工人员和作业车辆统计<br>安全帽、反光衣等PPE检测<br>人员进入危险区域检测<br>起重设备周边人员检测<br>矿区车辆及机械设备调度监测<br>边坡落石、裂缝和坍塌检测<br>非法采矿、非法占地目标发现<br>施工现场火灾和烟雾检测 | 判断现场有哪些目标<br>确定目标所在位置<br>判断人员、车辆或设备是否违反安全规则 | |
| | 4 | **公共安全、应急与安防** | 将消防、搜救、灾害响应、边境和园区安防整合为安全应用领域 | 人员、失踪者、受困人员<br>车辆、船只<br>明火、烟雾和高温热点<br>聚集人群<br>越界人员<br>可疑车辆或可疑物体<br>灾区生命目标<br>夜间移动目标 | 山区、森林和水域搜救<br>红外人体检测<br>火点、烟雾和复燃点检测<br>洪水、地震等灾害后的人员和车辆搜索<br>园区、边境及重要设施周界巡逻<br>人群聚集和异常活动检测<br>夜间入侵人员检测<br>危险区域非法进入检测 | 红外相机可用于夜间人员检测<br>烟雾遮挡环境中的热目标发现<br>森林或复杂地形中的生命目标搜索<br>火灾热点定位 | |
| | 5 | **交通与智慧城市** | 关注道路、城市空间及交通系统中的动态目标检测 | 小汽车、卡车、公交车<br>摩托车、自行车<br>行人<br>停放车辆<br>交通事故车辆<br>道路障碍物<br>抛洒物、落石<br>占道施工设备 | 车辆检测、分类和计数<br>车流量和道路拥堵分析<br>车辆轨迹和速度分析<br>违停、逆行和异常停车检测<br>交通事故自动发现<br>道路障碍物和抛洒物检测<br>大型活动人群数量统计<br>停车场车位占用检测 | 目标检测负责发现目标<br>多目标跟踪负责持续识别同一目标<br>可进一步计算车速、轨迹、停留时间和交通流量 | |
| | 6 | **海洋���水域与环境执法** | 将海岸、河流、湖泊、港口及海上监管统一归入水域监测 | 船舶、渔船和小型快艇<br>漂浮人员和救生设备<br>海洋动物<br>漂浮垃圾<br>水面油污和异常漂浮物<br>非法采砂船<br>非法捕捞船只<br>河道及岸边非法排放设施 | 船舶检测、分类和数量统计<br>非法捕捞及越界船只检测<br>水上搜救和落水人员检测<br>海洋动物检测和数量调查<br>河流、湖泊漂浮垃圾检测<br>水面异常目标和污染物发现<br>港口和海岸线安防巡逻<br>非法采砂、非法排放行为发现 | 重点解决水面反光、波浪干扰和远距离小目标问题<br>可结合长焦、红外和视频跟踪提升检测效果 | |
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| | 序号 | 卫星 Domain | 典型检测目标 | 多光谱数据与公开数据集依据 | 典型任务与输出 | |
| | -- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | |
| | 1 | **海事、航空与交通目标监测** | **海事目标：**船舶、渔船、大型货船、海上平台及港口目标<br><br>**航空目标：**飞机、直升机及机场地面航空器<br><br>**飞机细粒度类型：**Boeing 737、747、777、787，Airbus A220、A321、A330、A350，ARJ21、C919及其他飞机类型<br><br>**飞机属性：**机身长度、翼展、机翼形状、机翼位置、动力类型、发动机数量、垂尾数量及飞机用途<br><br>**交通设施：**机场、跑道、停机坪、港口、船厂、道路桥梁和铁路桥梁 | **S2-SHIPS**使用Sentinel-2多光谱影像开展船舶检测，说明船舶是原生多光谱目标检测中相对成熟的方向。([MDPI][1])<br><br>**fMoW-full**提供4波段和8波段可见光—近红外影像，可支持机场、港口、船厂和交通设施等目标级识别。([GitHub][2])<br><br>**FAIR1M**提供旋转框细粒度遥感目标检测，明确要求模型不仅检测“飞机”，还要识别Airbus A350、Boeing 747等具体类型。([ar5iv][3])<br><br>**RarePlanes**提供飞机检测以及10类细粒度结构属性标注。([arXiv][4]) | 船舶和飞机定位、分类、计数<br>飞机具体型号识别<br>飞机结构属性与用途分类<br>飞机停放位置及朝向检测<br>船舶尺寸、方向和航行状态估计<br>机场、港口及交通枢纽活动监测 | |
| | 2 | **城市建筑与公共基础设施** | 建筑物、住宅区和大型厂房<br>施工场地及临时建筑<br>机场和公共服务设施<br>道路、桥梁、水坝和交通节点<br>受损、倒塌或被洪水影响的建筑物 | **fMoW-full**的多光谱数据覆盖住宅、施工场地、医院、学校、公共机构、桥梁和水坝等设施类别。([GitHub][2])<br><br>**SatlasPretrain**融合Sentinel-2与高分辨率航片，提供建筑、机场、道路等多种点、线和多边形标注。([CVF Open Access][5]) | 建筑物和基础设施定位<br>设施数量及空间分布统计<br>新增建筑和施工活动发现<br>公共设施识别<br>灾前灾后建筑目标对比 | |
| | 3 | **能源、工业与资源设施** | 光伏电站和光伏阵列<br>风力发电机及风电场<br>储油罐、储罐区和炼化设施<br>发电厂、变电站及工业园区<br>海上油气平台<br>矿区、采石场和资源开发设施<br>水处理和垃圾处理设施 | **fMoW-full**包含光伏电站、风电场、发电厂、变电站、油气设施、储罐、露天矿和水处理设施等多光谱目标类别。([GitHub][2])<br><br>**SatlasPretrain**包含风机点目标、光伏场区域目标、建筑和其他基础设施标注，并使用Sentinel-2等卫星数据。([CVF Open Access][5]) | 能源资产检测和计数<br>工业设施定位与分类<br>风机、光伏场和海上平台普查<br>储罐及大型设备统计<br>资源开发活动和设施变化监测 | |
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| [1]: https://www.mdpi.com/2072-4292/13/21/4255?utm_source=chatgpt.com "Ship Detection in Sentinel 2 Multi-Spectral Images with Self ..." |
| [2]: https://github.com/fMoW/dataset?utm_source=chatgpt.com "fMoW/dataset" |
| [3]: https://ar5iv.org/pdf/2103.05569 "[2103.05569] FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery" |
| [4]: https://arxiv.org/abs/2006.02963 "[2006.02963] RarePlanes: Synthetic Data Takes Flight" |
| [5]: https://openaccess.thecvf.com/content/ICCV2023/papers/Bastani_SatlasPretrain_A_Large-Scale_Dataset_for_Remote_Sensing_Image_Understanding_ICCV_2023_paper.pdf?utm_source=chatgpt.com "SatlasPretrain: A Large-Scale Dataset for Remote Sensing ..." |
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| ## Dataset Structure |
| | Domain | Dataset | Task | Output Format | Path | Source | Modality | Included| |
| |---------|---------|-------------|---------------|------|------|------|------| |
| | 1-1 | AgroVG | Agricultural Visual Grounding | Box | `ObjEarth-Data/AgroVG/t1` | [here](https://huggingface.co/datasets/sauryrs/AgroVG) | RGB | ✅ | |
| | 1-1 | AgroMind | agricultural remote sensing benchmark(13 tasks) | *** | `ObjEarth-Data/AgroMind/` | [here](https://huggingface.co/datasets/AgroMind/AgroMind) | RGB | ✅ | |
| | 1-1 | gRef-CW | Generalised Visual Grounding in Agriculture | Box | `ObjEarth-Data/gRef-CW/` - `ObjEarth-Bench/gRef-CW/` | [here](https://huggingface.co/datasets/Mhaghighat98/gRef-CW) | RGB | ✅ | |
| | 1-1 | DroneBird | Bird detection, counting, and tracking | Box | `ObjEarth-Data/DroneBird/` | [here](https://visdrone.org/visdrone/data-base/dronebird) | RGB | ✅ | |
| | 1-1 | AnimalDrone | Animal detection and counting | Box | `ObjEarth-Data/AnimalDrone/` | [here](https://visdrone.org/visdrone/data-base/animaldrone) | RGB | ✅ | |
| | 1-1 | TinyTrees | Tree counting | point | `ObjEarth-Data/tinytrees/` | [here](https://github.com/dgominski/treematch) | satellite imagery | ✅ | |
| | 1-2 | STN-PLAD |Power Line Assets Detection | Box | `ObjEarth-Data/STN-PLAD` | [here](https://github.com/andreluizbvs/PLAD) | RGB | ✅ | |
| | 1-2 | InsPLAD | Power Line Asset Inspection | Box | `ObjEarth-Data/InsPLAD` | [here](https://github.com/andreluizbvs/InsPLAD) | RGB | ✅ | |
| | 1-2 | WTBD | Wind Turbine Blade Detection | Box | `ObjEarth-Data/WTBD` | [here](https://springernature.figshare.com/articles/dataset/Multiclass_Dataset_for_Intelligent_Detection_of_Wind_Turbine_Blade_Defects_Using_Drone_Imagery/30210175) | RGB | ✅ | |
| | 1-2 | TTPLA | Detection and Segmentation of Transmission Towers and Power Lines | Box+Mask | `ObjEarth-Data/TTPLA` | [here](https://github.com/R3ab/ttpla_dataset) | RGB | ✅ | |
| | 1-3 | UAVConstructionSite | UAV Construction Site Image Captioning | None | `ObjEarth-Data/UAVConstructionSite` | [here](https://data.mendeley.com/datasets/4h68fmktwh/1) | RGB | ✅ | |
| | 1-4 | uav_dataset | Person Detection Covered with Heavy Snow (Search and Rescue) | Box | `` | [here](https://www.kaggle.com/datasets/imbalet/uav-dataset) | RGB | ⏳ | |
| | 1-4 | FLAME2-DT | Wildland Fire Detection | Box | `ObjEarth-Data/FLAME2-DT/` | [here](https://ieee-dataport.org/documents/flame2-dt#files) | RGB+Infrared |✅ | |
| | 1-4 | SARD | Person Detection in Search and Rescue Scenarios in Drone | Box | `ObjEarth-Data/SARD-fixed` | [here](https://ieee-dataport.org/documents/search-and-rescue-image-dataset-person-detection-sard) | RGB | ✅| |
| | 1-4 | RescueNet | Post Disaster UAV Dataset for Semantic Segmentation | Mask | `ObjEarth-Data/RescueNet` | [here](https://github.com/BinaLab/RescueNet-A-High-Resolution-Post-Disaster-UAV-Dataset-for-Semantic-Segmentation) | RGB | ✅ | |
| | 1-5 | UAV-SEG-2025 | UAV Pavement Distress Detection | Box | `ObjEarth-Data/UAV-SEG-2025` | [here](https://www.kaggle.com/datasets/patelravi07/enhanced-uav-pdd) | RGB | ✅ | |
| | 1-5 | UAVVaste | UAV Trash and Litter Detection | Box | `ObjEarth-Data/UAVVaste/` | [here](https://www.kaggle.com/datasets/vexxingbanana/uavvaste-dataset) | RGB | ✅ | |
| | 1-5 | RefDrone | Drone Object Referring | Box | `ObjEarth-Data/VisDrone2019_DET/` - `ObjEarth-Bench/RefDrone/`| [here](https://github.com/sunzc-sunny/refdrone) | RGB | ✅ | |
| | 1-5 | VisDrone2019_DET | Drone Object Detection | Box | `ObjEarth-Data/VisDrone2019_DET/` | | RGB | ✅ | |
| | 1-5 | HazyDet | Drone Object Detection in Hazy | Box | `ObjEarth-Data/HazyDet/` | | RGB |✅ | |
| | 1-6 | AFO | Aerial Floating Object Detection (Maritime Search and Rescue) | Box | `ObjEarth-Data/AFO` | [here](https://www.kaggle.com/datasets/jangsienicajzkowy/afo-aerial-dataset-of-floating-objects) | RGB | ✅ | |
| | 1-6 | FloPWD | Floating Plastic Waste Segmentation | Mask | `ObjEarth-Data/FloPWD` | [here](https://data.mendeley.com/datasets/znxjncgjkc/2) | RGB | ✅ | |
| | 2-1 | VHRShips | Ship Detection | Box | `ObjEarth-Data/VHRShips` | [here](https://github.com/radres333/VHRShips) | Optical satellite images | ✅ | |
| | 2-1 | HRSID | Ship Detection | Box | `ObjEarth-Data/HRSID` | [here](https://github.com/chaozhong2010/HRSID) | SAR | ✅ | |
| | 2-1 | MAR20 | Military Aircraft Detection | Box | `ObjEarth-Data/MAR20` | [here](https://1drv.ms/u/s!AmgKYzARBl5ceGUKiVsRzfxZa_4?e=K21Spg) | RGB | ✅ | |
| | 2-1 | FAIR1M (1,732 images been released) | Object Detection in High-Resolution Satellite Images| Box | `ObjEarth-Data/FAIR1M` | [here](https://huggingface.co/datasets/blanchon/FAIR1M) | RGB | ✅ | |
| | 2-1 | DOTA | Object Detection in Aerial Images | Box | `ObjEarth-Data/DOTA` | [here](https://captain-whu.github.io/DOTA/dataset) | RGB | ✅ | |
| | 2-3 | OPMOD | Open Pit Mine Object Detection | Box | `ObjEarth-Data/OPMOD` | [here](https://figshare.com/articles/dataset/Open_Pit_Mine_Object_Detection_Dataset/27300960) | Optical satellite images | ✅ | |
| | 2-3 | MARIDA | Marine Debris Segmentation | Mask | `ObjEarth-Data/MARIDA` | [here](https://zenodo.org/records/5151941) | Multispectral Sentinel-2 | ✅ | |
| | 2-3 | MADOS | Marine Debris and Oil Spill Segmentation | Mask | `ObjEarth-Data/MADOS` | [here](https://zenodo.org/records/10664073) | Multispectral Sentinel-2 | ✅ | |
| | 2-3 | FASDD_RS | Flame and Smoke Detection | Box | `ObjEarth-Data/FASDD_RS` | [here](https://www.scidb.cn/detail?dataSetId=ce9c9400b44148e1b0a749f5c3eb0bda) | Optical satellite images | ✅ | |
| | | DVGBench | UAV Visual Grounding | Box | `ObjEarth-Data/DVG_Bench/images/` - `ObjEarth-Bench/DVGBench/` | [here](https://github.com/VisionXLab/DVGBench) | RGB | ✅ | |
| | | TriModalDet | UAV Object Detection | Box | `ObjEarth-Data/TriModalDet/` | [here](https://github.com/radlab-sketch/trimodal-uav-det) | RGB–Thermal–Event camera | ✅| |
| | 1-5 | AU-AIR | UAV Object Detection (Traffic Surveillance)| Box | `ObjEarth-Data/AU-AIR/images/` | | RGB | ⏳ | |
| | 1-3 | CIS | Construction Instance Segmentation | Box+Mask | `` | [here](https://github.com/XZ-YAN/CIS-Dataset) | RGB | * | |
| | 1-3 | ELDOR | Illegal Gold Mining | Mask | `` | [here](https://huggingface.co/datasets/IRSC/ELDOR) | RGB | yzh | |
| | 1-4 | TinyPerson | Person Detection for Maritime Rescue | Box | `` | [here](https://github.com/ucas-vg/PointTinyBenchmark) | RGB | yzh | |
| | 1-4 | FASDD_UAV | Flame and Smoke Detection | Box | `` | [here](https://www.scidb.cn/detail?dataSetId=ce9c9400b44148e1b0a749f5c3eb0bda) | RGB | ⏳ | |
| | 1-6 | MOBDrone | Man OverBoard Rescue Detection | Box | `` | [here](https://aimh.isti.cnr.it/dataset/MOBDrone/) | RGB | | |
| | 1-6 | SeaDronesSee | Human Detection (Maritime Search and Rescue) | Box | `` | [here](https://github.com/Ben93kie/SeaDronesSee) | RGB | | |
| | 1-6 | AVD | Aerial Vessels Detection | Box | `` | [here](https://zenodo.org/records/7076145) | RGB | * | |
| | 1-6 | UAVPorpoises | Drone-based Porpoise Tracking | Box | `` | [here](https://putvision.github.io/UAVPorpoises/) | Video | * | |
| | 2-1 | ShipRSImagaeNet | Ship Detection | Box | `` | [here](https://github.com/zzndream/ShipRSImageNet) | Optical satellite images | liji | |
| | 2-1 | xView3-SAR | Ship Detection | Box | `` | [here](https://iuu.xview.us/) | SAR | * | |
| | 2-1 | xView | Object Detection for Overhead Imagery | Box | `` | [here](https://xviewdataset.org/) | RGB | * | |
| | 2-1 | DIOR | Object Detection | Box | `` | [here](https://drive.google.com/drive/folders/1UdlgHk49iu6WpcJ5467iT-UqNPpx__CC) | RGB | ⏳ | |
| | 2-1 | RarePlanes | Aircraft Detection | Box | `` | [here](https://www.iqt.org/library/the-rareplanes-dataset) | Optical satellite images | * | |
| | 2-1 | SADD | SAR Aircraft Detection | Box | `` | [here](https://github.com/hust-rslab/SAR-aircraft-data) | SAR | yzh | |
| | 2-1 | LEVIR-Ship | Tiny Ship Detection | Box | `` | [here](https://github.com/WindVChen/LEVIR-Ship) | RGB | yzh | |
| | 2-1 | fMoW-full (3.5TB) | Object Detection | Box | `` | [here](https://github.com/fMoW/dataset) | Multispectral Imagery | * | |
| | 2-1 | fMoW-rgb (200GB) | Object Detection | Box | `` | [here](https://github.com/fMoW/dataset) | RGB | * | |
| | 2-1 | SatlasPretrain | large-scale pre-training dataset for remote sensing image understanding | points, polygons, polylines, properties, segmentation labels, regression labels, and classification labels | `` | [here](https://huggingface.co/allenai/satlas-pretrain) | RGB | * | |
| | 2-1 | | | | `` | [here]() | | ❌ | |
| | 2-1 | | | | `` | [here]() | | ❌ | |
| | | | | | `` | | | ❌ | |
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