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| 1 |
+
```markdown
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| 2 |
+
# Categories & Links
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| 3 |
+
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| 4 |
+
## Urban / Land Use / Settlement / Street Blocks
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| 5 |
+
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| 6 |
+
1. **Global Urban Land Uses (10 m) with Sentinel‑2 + POI prompting (2025)**
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| 7 |
+
Global 10 m urban land-use dataset with pixel-level multi-class masks, produced from Sentinel‑2 imagery and OSM POI prompts.
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| 8 |
+
Paper: https://www.sciencedirect.com/science/article/abs/pii/S0034425725002287
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| 9 |
+
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| 10 |
+
2. **Global Urban Open Space for 169 Megacities (1.19 m) (2025)**
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| 11 |
+
High-resolution (1.19 m) semantic mapping of urban open space across 169 megacities with pixel-level classes (parks/green space, sports, transportation space, water, background).
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| 12 |
+
Paper: https://www.nature.com/articles/s41597-025-04924-x
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| 13 |
+
Code: https://github.com/RunyuFan/OpenspaceGlobal
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| 14 |
+
Data: https://www.scidb.cn/en/detail?dataSetId=ddc0dd716dd740f0ae74a113e207bb64
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| 15 |
+
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| 16 |
+
3. **GURS: Global Urban and Rural Settlement Dataset (100 m, 2000–2020) (2024)**
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| 17 |
+
Global 100 m settlement product distinguishing urban vs rural areas for multiple years (GeoTIFF layers).
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| 18 |
+
Paper: https://www.nature.com/articles/s41597-024-04195-y
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| 19 |
+
Code: https://github.com/liuzht7-github/GURS.git
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| 20 |
+
Data: https://zenodo.org/records/11160893
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| 21 |
+
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| 22 |
+
4. **Multi-level Street-Block Divisions for 985 Cities Worldwide (2025)**
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| 23 |
+
Global street-block division vectors for 985 cities with a uniform division rationale for consistent cross-city analysis.
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| 24 |
+
Paper: https://www.nature.com/articles/s41597-025-04704-7
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| 25 |
+
Data: https://figshare.com/articles/dataset/MSDCW_Dataset_and_Code/26021314
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| 26 |
+
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| 27 |
+
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| 28 |
+
## Buildings / Built Environment
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| 29 |
+
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| 30 |
+
5. **CMAB: Multi-Attribute Building Dataset of China (2025)**
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| 31 |
+
Nationwide instance-level building dataset for China with rich attributes (e.g., rooftop, height, structure, function, style, age, quality).
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| 32 |
+
Paper: https://www.nature.com/articles/s41597-025-04730-5
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| 33 |
+
Data: https://figshare.com/articles/dataset/CMAB-The_World_s_First_National-Scale_Multi-Attribute_Building_Dataset/27992417/2
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| 34 |
+
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| 35 |
+
6. **3D-GloBFP: Global 3D Building Footprints (2024)**
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| 36 |
+
Global building footprint resource providing 3D/height-related building information (Zenodo release).
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| 37 |
+
Paper: https://essd.copernicus.org/articles/16/5357/2024/essd-16-5357-2024-discussion.html
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| 38 |
+
Data: https://doi.org/10.5281/zenodo.11319912
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| 39 |
+
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| 40 |
+
7. **Google Open Buildings Temporal v1 (GEE)**
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| 41 |
+
Temporal version of Google Open Buildings footprints (polygons + height/attributes) accessible via Google Earth Engine (see reference link).
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| 42 |
+
Reference: https://mp.weixin.qq.com/s/5zM2lQJJ_5GomoKjOAejvw
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| 43 |
+
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| 44 |
+
8. **Google Open Buildings v3 (GEE)**
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| 45 |
+
Global building footprint polygons (vector outlines), commonly accessed via Google Earth Engine (see reference link).
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| 46 |
+
Reference: https://mp.weixin.qq.com/s/5zM2lQJJ_5GomoKjOAejvw
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| 47 |
+
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| 48 |
+
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| 49 |
+
## Energy / Power Systems / Hydropower / Solar
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| 50 |
+
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| 51 |
+
9. **Global Photovoltaic (PV) Solar Panel Dataset (20 m, 2019–2022) (2025)**
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| 52 |
+
Annual global PV mapping product at 20 m resolution (TIFF), with validation vectors and additional annotated samples/original imagery.
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| 53 |
+
Paper: https://www.nature.com/articles/s41597-025-04985-y
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| 54 |
+
Data (Zenodo): https://doi.org/10.5281/zenodo.10684793
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| 55 |
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GEE App: https://cxh1216.users.earthengine.app/view/solarpv-bnu
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| 56 |
+
Validation: https://doi.org/10.5281/zenodo.14348427
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| 57 |
+
Samples/Images: https://doi.org/10.5281/zenodo.14922538
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| 58 |
+
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| 59 |
+
10. **Great Britain Primary Substations + Household Heating (2024)**
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| 60 |
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GB primary substation dataset (location/capacity/demand/parent nodes) integrated with household heating types from census data.
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| 61 |
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Paper: https://www.sciencedirect.com/science/article/pii/S2352340924004529
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| 62 |
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Code: https://github.com/HDRah/Data-PS-GB
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| 63 |
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Data: https://zenodo.org/records/10866260
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| 64 |
+
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| 65 |
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11. **GloHydroRes32: Global Hydropower Plants + Reservoirs (2025)**
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| 66 |
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Global tabular dataset combining open hydropower plant and reservoir sources (capacity, dam/reservoir attributes such as height, depth, area, volume).
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| 67 |
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Paper: https://www.nature.com/articles/s41597-025-04975-0
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| 68 |
+
Code: https://github.com/SustainableWaterSystems/GloHydroRes/tree/main
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| 69 |
+
Data: https://zenodo.org/records/14526360
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| 70 |
+
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| 71 |
+
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| 72 |
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## Climate / Meteorology / Water Balance / Carbon
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| 73 |
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| 74 |
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12. **China 1 km Monthly Mean Temperature (1901–2023)**
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| 75 |
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China-wide gridded monthly mean temperature dataset (~1 km, NetCDF), downscaled from global climate datasets and validated with independent stations.
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| 76 |
+
Data: https://data.tpdc.ac.cn/zh-hans/data/71ab4677-b66c-4fd1-a004-b2a541c4d5bf/
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| 77 |
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| 78 |
+
13. **TerraClimate: Monthly Climate & Climatic Water Balance (1958–2024) (GEE)**
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| 79 |
+
Global monthly climate and water-balance dataset (temperature, precipitation, radiation, wind, PET, runoff, soil moisture, etc.) available in Google Earth Engine.
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| 80 |
+
GEE Catalog: https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE?hl=zh-cn
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| 81 |
+
Reference: https://mp.weixin.qq.com/s/siDwbNtzyDZxOTTgu2WS5w
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| 82 |
+
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| 83 |
+
14. **China Forest AGB Time Series (30 m, 1985–2023) (Preprint)**
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| 84 |
+
Annual 30 m aboveground biomass (AGB) mapping time series for China; accessible via a GEE script and Zenodo releases split by period.
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| 85 |
+
Preprint: https://essd.copernicus.org/preprints/essd-2025-96/
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| 86 |
+
GEE Script: https://code.earthengine.google.com/4f8ad8d32ddb84e826e941a95f31f9be
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| 87 |
+
Zenodo (Parts):
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| 88 |
+
- https://doi.org/10.5281/zenodo.12620984
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| 89 |
+
- https://doi.org/10.5281/zenodo.12637101
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| 90 |
+
- https://doi.org/10.5281/zenodo.12655492
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| 91 |
+
- https://doi.org/10.5281/zenodo.12658255
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| 92 |
+
- https://doi.org/10.5281/zenodo.12742210
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| 93 |
+
- https://doi.org/10.5281/zenodo.12747329
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| 94 |
+
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| 95 |
+
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| 96 |
+
## Terrain / DEM
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| 97 |
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| 98 |
+
15. **GEDTM30: Global Ensemble Digital Terrain Model (30 m) (Preprint)**
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| 99 |
+
Global 30 m terrain model fused from ICESat‑2, GEDI, and multi-source inputs; also provides derived land-surface/topographic parameters at multiple scales.
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| 100 |
+
Preprint: https://www.researchsquare.com/article/rs-6280607/v1
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| 101 |
+
Code/Data: https://github.com/openlandmap/GEDTM30
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| 102 |
+
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| 103 |
+
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| 104 |
+
## Agriculture / Land Change
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| 105 |
+
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| 106 |
+
16. **Main Grain Land (MGL) Changes in China (30 m, 1985–2020) (2024)**
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| 107 |
+
30 m change-mapping dataset for China’s main grain land from 1985–2020, supporting crop distribution and land-change analysis.
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| 108 |
+
Paper: https://www.nature.com/articles/s41597-024-04292-y
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| 109 |
+
Data: https://doi.org/10.6084/m9.figshare.26212643.v2
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| 110 |
+
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| 111 |
+
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| 112 |
+
## Hydrology / Flood / Rivers
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| 113 |
+
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| 114 |
+
17. **FloodCastBench (2025)**
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| 115 |
+
Large-scale dataset and foundation-model benchmark for flood modeling/forecasting; includes code to reproduce the dataset.
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| 116 |
+
Paper: https://www.nature.com/articles/s41597-025-04725-2
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| 117 |
+
Code: https://github.com/HydroPML/FloodCastBench
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| 118 |
+
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| 119 |
+
18. **HydroRIVERS (HydroSHEDS)**
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| 120 |
+
Global vector river network dataset widely used in hydrological and watershed analyses.
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| 121 |
+
Product: https://www.hydrosheds.org/products/hydrorivers
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| 122 |
+
Reference (Chinese): https://mp.weixin.qq.com/s/VZkdlHZB107EfE22QXKkuA
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| 123 |
+
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| 124 |
+
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| 125 |
+
## Hazards / Disaster Events
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| 126 |
+
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| 127 |
+
19. **Global Concurrent Hazards Inventory (1981–2020)**
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| 128 |
+
Global inventory and hotspot analysis of concurrent hazards compiled from multiple event databases (earthquake, storm, landslide, volcano, wildfire, flood).
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| 129 |
+
Paper: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024EF004490
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| 130 |
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| 131 |
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| 132 |
+
## Society / Mobility
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| 133 |
+
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| 134 |
+
20. **International Migration Flows (online-data derived) (2025)**
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| 135 |
+
Dataset estimating global international migration flows using online data signals; published on HDX.
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| 136 |
+
Paper: https://www.pnas.org/doi/10.1073/pnas.2409418122
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| 137 |
+
Data: https://data.humdata.org/dataset/international-migration-flows
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| 138 |
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| 139 |
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| 140 |
+
## Remote Sensing Foundation Models / Vision-Language / Benchmarks / Generation / Tracking
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| 141 |
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| 142 |
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21. **EarthView (2017–2022; ~15 TB) (WACV 2025)**
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| 143 |
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Large-scale multi-modal remote sensing dataset for self-supervised learning and foundation-model pretraining (hosted on Hugging Face).
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| 144 |
+
Paper: https://openaccess.thecvf.com/content/WACV2025W/CV4EO/papers/Velazquez_EarthView_A_Large_Scale_Remote_Sensing_Dataset_for_Self-Supervision_WACVW_2025_paper.pdf?utm_source=chatgpt.com
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| 145 |
+
Data: https://huggingface.co/datasets/satellogic/EarthView
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| 146 |
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| 147 |
+
22. **Falcon: Remote Sensing Vision-Language Foundation Model (Preprint)**
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| 148 |
+
Open vision-language foundation model for remote sensing with released code.
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| 149 |
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Paper: https://arxiv.org/abs/2503.11070
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Code: https://github.com/tianhuilab/falcon
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| 151 |
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| 152 |
+
23. **Text2Earth (Remote Sensing Text-to-Image Generation) (2025)**
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| 153 |
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Global-scale dataset and foundation model for text-driven remote sensing image generation.
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| 154 |
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Paper: https://ieeexplore.ieee.org/document/10988859
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| 155 |
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Project: https://chen-yang-liu.github.io/Text2Earth/
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| 156 |
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| 157 |
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24. **MMM-RS (NeurIPS 2024) Text-to-Image Benchmark**
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| 158 |
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Multi-modal, multi-GSD, multi-scene remote sensing dataset and benchmark for text-to-image generation.
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| 159 |
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Paper: https://arxiv.org/abs/2410.22362
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| 160 |
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Dataset/Repo: https://github.com/ljl5261/MMM-RS
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| 161 |
+
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| 162 |
+
25. **VRSBench (2024)**
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| 163 |
+
Versatile vision-language benchmark dataset for remote sensing image understanding (data/code via project page).
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| 164 |
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Paper: https://arxiv.org/abs/2406.12384
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| 165 |
+
Project: https://vrsbench.github.io
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| 166 |
+
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| 167 |
+
26. **UrBench (AAAI 2025)**
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| 168 |
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Benchmark for evaluating large multimodal models in multi-view urban scenario understanding.
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| 169 |
+
Paper: https://arxiv.org/abs/2408.17267
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| 170 |
+
Project: https://opendatalab.github.io/UrBench/
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| 171 |
+
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| 172 |
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27. **RSTeller (Remote Sensing Captioning / VLM Scaling)**
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| 173 |
+
Resource for scaling remote sensing vision-language modeling using rich linguistic semantics derived from open data and LLMs.
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| 174 |
+
Paper: https://arxiv.org/abs/2408.14744
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| 175 |
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Code: https://github.com/SlytherinGe/RSTeller
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| 176 |
+
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| 177 |
+
28. **GlobalGeoTree (Tree Species; Vision-Language) (Preprint)**
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| 178 |
+
Multi-granular vision-language dataset for global tree species classification (curated from sources such as GBIF); released on GitHub and Hugging Face.
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| 179 |
+
Paper: https://arxiv.org/pdf/2505.12513
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| 180 |
+
Code: https://github.com/MUYang99/GlobalGeoTree
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| 181 |
+
Data (HF): https://huggingface.co/datasets/yann111/GlobalGeoTree
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| 182 |
+
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| 183 |
+
29. **EarthDial (CVPR 2025)**
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| 184 |
+
Project for interactive dialogue over multi-sensory Earth observations (vision-language style).
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| 185 |
+
Repo: https://github.com/hiyamdebary/EarthDial
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| 186 |
+
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| 187 |
+
30. **EEG + Eye-Tracking Dataset for Remote Sensing Object Detection (2025)**
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| 188 |
+
Human experiment dataset with simultaneous EEG and eye-tracking collected during remote sensing object detection tasks; includes code for modeling/validation.
|
| 189 |
+
Paper: https://www.nature.com/articles/s41597-025-04995-w
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| 190 |
+
Code (classification): https://github.com/Bing-1997/EEGET_RSOD/tree/main/eegnet
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| 191 |
+
Code (validation): https://github.com/Bing-1997/EEGET_RSOD
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| 192 |
+
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| 193 |
+
31. **MUST: Multispectral UAV Single Object Tracking (CVPR 2025)**
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| 194 |
+
Multispectral UAV single-object tracking dataset and unified framework for aerial/remote sensing tracking tasks.
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| 195 |
+
Paper: https://arxiv.org/abs/2503.17699
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| 196 |
+
Code: https://github.com/q2479036243/MUST-Multispectral-UAV-Single-Object-Tracking
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| 197 |
+
```
|