GDAL

Rapid butterfly declines across the United States during the 21st century

Edwards, C. B., Zipkin, E. F., Henry, E. H., Haddad, N. M., Forister, M. L., Burls, K. J., Campbell, S., Crone, E. E., Diffendorfer, J. E., Douglas, M. R., et al. (2025) · Science · doi:10.1126/science.adp4671 · Cited by 104

sf: Simple Features for R

Pebesma, E. J. (2016) · doi:10.32614/cran.package.sf · Cited by 90

DeltaDTM: A global coastal digital terrain model

Pronk, M., Hooijer, A., Eilander, D., Haag, A. V., de Jong, T., Vousdoukas, M. I., Vernimmen, R., Ledoux, H., & Eleveld, M. A. (2024) · Scientific Data · doi:10.1038/s41597-024-03091-9 · Cited by 85

TorchGeo

Stewart, A. J., Robinson, C., Corley, I., Ortiz, A., Ferres, J. L., & Banerjee, A. (2022) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3557915.3560953 · Cited by 80

Wall-to-wall mapping of tree extent in the tropics with Sentinel-1 and Sentinel-2

Brandt, J., Ertel, J., Spore, J., & Stolle, F. (2023) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113574 · Cited by 77

Australian human-induced native forest regeneration carbon offset projects have limited impact on changes in woody vegetation cover and carbon removals

Macintosh, A., Butler, D. W., Larraondo, P., Evans, M., Ansell, D., Waschka, M., Fensham, R. J., Eldridge, D. J., Lindenmayer, D. B., Gibbons, P., et al. (2024) · Communications Earth & Environment · doi:10.1038/s43247-024-01313-x · Cited by 76

Validation of FABDEM, a global bare-earth elevation model, against UAV-lidar derived elevation in a complex forested mountain catchment

Marsh, C. B., Harder, P., & Pomeroy, J. W. (2023) · Environmental Research Communications · doi:10.1088/2515-7620/acc56d · Cited by 72

Multi-resolution gridded maps of vegetation structure from GEDI

Burns, P., Hakkenberg, C. R., & Goetz, S. J. (2024) · Scientific Data · doi:10.1038/s41597-024-03668-4 · Cited by 60

HydroMT: Automated and reproducible model building and analysis

Eilander, D., Boisgontier, H., Bouaziz, L. J. E., Buitink, J., Couasnon, A., Dalmijn, B., Hegnauer, M., de Jong, T., Loos, S., Marth, I., et al. (2023) · The Journal of Open Source Software · doi:10.21105/joss.04897 · Cited by 49

In-orbit demonstration of a re-trainable machine learning payload for processing optical imagery

Mateo‐García, G., Veitch-Michaelis, J., Purcell, C. R., Longépé, N., Reid, S., Anlind, A., Bruhn, F. C., Parr, J., & Mathieu, P.-P. (2023) · Scientific Reports · doi:10.1038/s41598-023-34436-w · Cited by 49

MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022)

Pan, F., Jiang, L., Wang, G., Pan, J., Huang, J., Zhang, C., Cui, H., Yang, J., Zheng, Z., Wu, S., et al. (2024) · Earth system science data · doi:10.5194/essd-16-2501-2024 · Cited by 49

Estimating the effects of vegetation and increased albedo on the urban heat island effect with spatial causal inference

Calhoun, Z. D., Willard, F., Ge, C., Rodriguez, C., Bergin, M. H., & Carlson, D. (2024) · Scientific Reports · doi:10.1038/s41598-023-50981-w · Cited by 48

TorchGeo: Deep Learning With Geospatial Data

Stewart, A. J., Robinson, C., Corley, I., Ortiz, A., Ferres, J. L., & Banerjee, A. (2024) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3707459 · Cited by 52

Alkalinity generation from carbonate weathering in a silicate-dominated headwater catchment at Iskorasfjellet, northern Norway

Lehmann, N., Lantuit, H., Böttcher, M. E., Hartmann, J., Eulenburg, A., & Thomas, H. (2023) · Biogeosciences · doi:10.5194/bg-20-3459-2023 · Cited by 47

Deep time spatio‐temporal data analysis using py GP lates with P late T ectonic T ools and GP lately

Mather, B., Müller, R. D., Zahirovic, S., Cannon, J., Chin, M., Ilano, L., Wright, N. M., Alfonso, C., Williams, S. E., Tetley, M. G., et al. (2023) · Geoscience Data Journal · doi:10.1002/gdj3.185 · Cited by 40

A Biologist’s Guide to the Galaxy: Leveraging Artificial Intelligence and Very High-Resolution Satellite Imagery to Monitor Marine Mammals from Space

Khan, C. B., Goetz, K. T., Cubaynes, H. C., Robinson, C., Murnane, E., Aldrich, T., Sackett, M., Clarke, P. J., Larue, M. A., White, T., et al. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11030595 · Cited by 38

The March of the Human FootprintPreprint

Sanderson, E. W., Fisher, K., Robinson, N., Sampson, D., Duncan, A., & Royte, L. (2022) · doi:10.32942/osf.io/d7rh6 · Cited by 36

Are Plant Functional Types Fit for Purpose?

Page, J. C., Abramowitz, G., Kauwe, M. G. D., & Pitman, A. J. (2023) · Geophysical Research Letters · doi:10.1029/2023gl104962 · Cited by 32

GeoBenchX: Benchmarking LLMs in Agent Solving Multistep Geospatial Tasks

Krechetova, V., & Kochedykov, D. (2025) · doi:10.1145/3764915.3770721 · Cited by 29

EcoDes-DK15: high-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set

Assmann, J. J., Moeslund, J. E., Treier, U. A., & Normand, S. (2022) · Earth system science data · doi:10.5194/essd-14-823-2022 · Cited by 27

The coevolutionary mosaic of bat betacoronavirus emergence risk

Forero, N., Muylaert, R. L., Seifert, S. N., Albery, G. F., Becker, D. J., Carlson, C. J., & Poisot, T. (2023) · Virus Evolution · doi:10.1093/ve/vead079 · Cited by 27

Automated snow avalanche release area delineation in data-sparse, remote, and forested regions

Sykes, J., Haegeli, P., & Bühler, Y. (2022) · Natural hazards and earth system sciences · doi:10.5194/nhess-22-3247-2022 · Cited by 26

A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal EnvironmentsPreprint

Buscombe, D., Wernette, P. A., Nicole, F. S., Favela, J. J., Goldstein. Evan, B., & Enwright, N. M. (2022) · EarthArXiv (California Digital Library) · doi:10.1038/s41597-023-01929-2

Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland

Jackisch, R., Heincke, B. H., Zimmermann, R., Sørensen, E. V., Pirttijärvi, M., Kirsch, M., Salmirinne, H., Lode, S., Kuronen, U., & Gloaguen, R. (2022) · Solid Earth · doi:10.5194/se-13-793-2022 · Cited by 21

Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA

Bassah, R., Corzo, G., Bhattacharya, B., Haider, S. M., Swain, E. D., & Aumen, N. G. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.132195 · Cited by 20

Evaluating pySTEPS optical flow algorithms for convection nowcasting over the Maritime Continent using satellite data

Smith, J. G., Birch, C. E., Marsham, J. H., Peatman, S., Bollasina, M. A., & Pankiewicz, G. S. (2024) · Natural hazards and earth system sciences · doi:10.5194/nhess-24-567-2024 · Cited by 20

Analyzing the sensitivity of a blowing snow model (SnowPappus) to precipitation forcing, blowing snow, and spatial resolution

Haddjeri, A., Baron, M., Lafaysse, M., Toumelin, L. L., Deschamps‐Berger, C., Vionnet, V., Gascoin, S., Vernay, M., & Dumont, M. (2024) · The Cryosphere · doi:10.5194/tc-18-3081-2024 · Cited by 19

Quasi-Linear Convective Systems and Derechos across Europe: Climatology, Accompanying Hazards, and Societal Impacts

Surowiecki, A., Pilguj, N., Taszarek, M., Piasecki, K., Púčik, T., & Brooks, H. E. (2024) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-23-0257.1 · Cited by 19

High‐Resolution Maps of Near‐Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning

Thaler, E. A., Uhlemann, S., Rowland, J. C., Schwenk, J., Wang, C., Dafflon, B., & Bennett, K. E. (2023) · Earth and Space Science · doi:10.1029/2023ea003015 · Cited by 17

Evaluation of high-resolution snowpack simulations from global datasets and comparison with Sentinel-1 snow depth retrievals in the Sierra Nevada, USA

Sourp, L., Gascoin, S., Jarlan, L., Pedinotti, V., Bormann, K. J., & Baba, M. W. (2025) · Hydrology and earth system sciences · doi:10.5194/hess-29-597-2025 · Cited by 17

Increased crevassing across accelerating Greenland Ice Sheet margins

Chudley, T. R., Howat, I. M., King, M. D., & MacKie, E. J. (2025) · Nature Geoscience · doi:10.1038/s41561-024-01636-6 · Cited by 17

Soil2Cover: Coverage path planning minimizing soil compaction for sustainable agriculture

Mier, G., Vélez, S., Valente, J. R. P., & de Bruin, S. (2025) · Precision Agriculture · doi:10.1007/s11119-025-10250-4 · Cited by 16

High-resolution digital outcrop model of the faults, fractures, and stratigraphy of the Agardhfjellet Formation cap rock shales at Konusdalen West, central Spitsbergen

Betlem, P., Birchall, T., Lord, G. S., Oldfield, S. J., Nakken, L., Ogata, K., & Senger, K. (2024) · Earth system science data · doi:10.5194/essd-16-985-2024 · Cited by 16

Associations between long-term air pollution exposure and mortality and cardiovascular morbidity: A comparison of mobility-integrated and residential-only exposure assessment

Ndiaye, A., Vienneau, D., Flückiger, B., Probst-Hensch, N. M., Jeong, A., Imboden, M., Schmitz, O., Lü, M., Vermeulen, R. C. H., Kyriakou, K., et al. (2025) · Environment International · doi:10.1016/j.envint.2025.109387 · Cited by 16

New approach to assess forest fragmentation based on multiscale similarity index

Netzel, P., Tymińska-Czabańska, L., Feleha, D. D., & Socha, J. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2023.111530 · Cited by 15

Mapping Daily Air Temperature Over the Hawaiian Islands From 1990 to 2021 via an Optimized Piecewise Linear Regression Technique

Kodama, K. M., Kourkchi, E., Longman, R. J., Lucas, M. P., Bateni, S. M., Huang, Y., Kagawa-Viviani, A. K., McLean, J. H., Cleveland, S. B., & Giambelluca, T. W. (2024) · Earth and Space Science · doi:10.1029/2023ea002851 · Cited by 15

Technical Note: Resolution enhancement of flood inundation grids

Bryant, S., Schumann, G. J.-P., Apel, H., Kreibich, H., & Merz, B. (2024) · Hydrology and earth system sciences · doi:10.5194/hess-28-575-2024 · Cited by 15

Evaluating Distributed Snow Model Resolution and Meteorology Parameterizations Against Streamflow Observations: Finer Is Not Always Better

Barnhart, T. B., Putman, A., Heldmyer, A. J., Rey, D. M., Hammond, J. C., Driscoll, J. M., & Sexstone, G. A. (2024) · Water Resources Research · doi:10.1029/2023wr035982 · Cited by 15

Hydroclimatic Vulnerability of Wetlands to Upwind Land Use Changes

Fahrländer, S. F., Wang‐Erlandsson, L., Pranindita, A., & Jaramillo, F. (2024) · Earth s Future · doi:10.1029/2023ef003837 · Cited by 14

Contemporary and historical detection of small lakes using super resolution Landsat imagery: promise and peril

Kyzivat, E. D., & Smith, L. C. (2023) · GIScience & Remote Sensing · doi:10.1080/15481603.2023.2207288 · Cited by 14

First estimates of fine root production in tropical peat swamp and terra firme forests of the central Congo Basin

Sciumbata, M., Mampouya, Y. E. W., Mbemba, M., Dargie, G. C., Baird, A. J., Morris, P. J., Ifo, S. A., Aerts, R., & Lewis, S. L. (2023) · Scientific Reports · doi:10.1038/s41598-023-38409-x · Cited by 13

Understanding and simulating cropland and non-cropland burning in Europe using the BASE (Burnt Area Simulator for Europe) model

Forrest, M., Hetzer, J., Billing, M., Bowring, S., Kosczor, E., Oberhagemann, L., Perkins, O., Warren, D. L., Arrogante‐Funes, F., Thonicke, K., et al. (2024) · Biogeosciences · doi:10.5194/bg-21-5539-2024 · Cited by 13

Automatic analysis of UAS-based thermal images to detect leakages in district heating systems

Vollmer, E., Volk, R., & Schultmann, F. (2023) · International Journal of Remote Sensing · doi:10.1080/01431161.2023.2242586 · Cited by 13

Enhancing oil palm segmentation model with GAN-based augmentation

Kwong, Q. B., Kon, Y. T., Rusik, W. R. W., Shabudin, M. N. A., Rahman, S. S. A., Kulaveerasingam, H., & Appleton, D. R. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00990-x · Cited by 11

VIPER Site Analysis

Beyer, R. A., Shirley, M., Colaprete, A., Fassett, C. I., Fernando, B. A., Himani, T. P., Lemelin, M., Camacho, J. M. M., Siegler, M. A., Annex, A. M., et al. (2025) · The Planetary Science Journal · doi:10.3847/psj/ae061a · Cited by 11

Spatially explicit predictions of food web structure from regional-level data

Dansereau, G., Barros, C., & Poisot, T. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2023.0166 · Cited by 11

Development of geospatial passability maps: A multi-criteria analysis approach

Potić, I., Stojanović, M., Ćurčić, N., Djordjević, D., & Banković, R. D. (2024) · Journal of the Geographical Institute Jovan Cvijic SASA · doi:10.2298/ijgi230822002p · Cited by 10

Land cover changes and comparison of current landscape metrics in a region of the Central Andes affected by population migration

Arizapana‐Almonacid, M. A., Pariona-Antonio, V. H., Castañeda-Tinco, M. I., Mendoza, J. C. A., Gutiérrez-Gómez, E., & Ramoni‐Perazzi, P. (2024) · Annals of GIS · doi:10.1080/19475683.2024.2304203 · Cited by 10

MiGIS: micromorphological soil and sediment thin section analysis using an open-source GIS and machine learning approach

Zickel, M., Gröbner, M., Röpke, A., & Kehl, M. (2024) · E&G Quaternary Science Journal · doi:10.5194/egqsj-73-69-2024 · Cited by 10

Mapping of peatlands in the forested landscape of Sweden using lidar-based terrain indices

Rimondini, L., Gumbricht, T., Ahlström, A., & Hugelius, G. (2023) · Earth system science data · doi:10.5194/essd-15-3473-2023 · Cited by 10

Improving lung cancer pathological hyperspectral diagnosis through cell-level annotation refinement

Yan, Z., Huang, H., Geng, R., Zhang, J., Chen, Y., & Nie, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-85678-9 · Cited by 9

Machine‐learning model to delineate sub‐surface agricultural drainage from satellite imagery

Redoloza, F. S., Williamson, T. N., Headman, A. O., & Allred, B. J. (2023) · Journal of Environmental Quality · doi:10.1002/jeq2.20493 · Cited by 9

Open Source – GIS

Löwe, P., Alfaro, A. A. A., Antonello, A., Baumann, P., Carrera, M., Durante, K., Hugentobler, M., Lime, S., Mitášová, H., Müller, R. D., et al. (2022) · Springer handbooks · doi:10.1007/978-3-030-53125-6_30 · Cited by 9

VOXEL-BASED POINT CLOUD SEGMENTATION AND BUILDING DETECTION

Hrutka, B. P., Siki, Z., & Takács, B. (2022) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-4-w1-2022-209-2022 · Cited by 9

Points for energy renovation (PointER): A point cloud dataset of a million buildings linked to energy features

Krapf, S., Mayer, K., & Fischer, M. A. (2023) · Scientific Data · doi:10.1038/s41597-023-02544-x · Cited by 9

Assessing Assisted Natural Regeneration as a Cost‐Efficient Mitigation for Climate Change and Biodiversity Loss in China

Qiu, M., Liu, D., Zhao, Y., Tong, Z., He, J., Fortin, M., & Huang, J. (2025) · Earth s Future · doi:10.1029/2024ef005257 · Cited by 8

Bayesian deep learning framework for updating landslide susceptibility assessment based on epistemic uncertainty with InSAR augmented samples

Chen, C., Dong, B., Lin, J., Shen, Z., Fang, L., Weng, Y., & Wang, K. (2025) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2025.07.008 · Cited by 8

Comparing NISAR (Using Sentinel-1), USDA/NASS CDL, and Ground Truth Crop/Non-Crop Areas in an Urban Agricultural Region

Kraatz, S., Lamb, B. T., Hively, W. D., Jennewein, J. S., Feng Gao, A., Cosh, M. H., & Siqueira, P. (2023) · Sensors · doi:10.3390/s23208595 · Cited by 8

Multisource Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in AlaSka (SIRIUS)

Kaiser, S., Boike, J., Grosse, G., & Langer, M. (2024) · Earth system science data · doi:10.5194/essd-16-3719-2024 · Cited by 8

Premature Deaths Due To Heat Exposure: The Potential Effects of Neighborhood‐Level Versus City‐Level Acclimatization Within US Cities

Shindell, D. T., Hunter, R., Faluvegi, G., & Parsons, L. A. (2024) · GeoHealth · doi:10.1029/2023gh000970 · Cited by 7

Hydrologic Evaluation of the Global Precipitation Measurement Mission over the U.S.: Effect of Spatial and Temporal Scales

Woods, D., Kirstetter, P., Vergara, H. J., Duarte, J. A., & Basara, J. B. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131134 · Cited by 7

Mapping fault geomorphology with drone-based lidar

Salomon, G., Finley, T. D., Nissen, E., Stephen, R., & Menounos, B. (2024) · Seismica · doi:10.26443/seismica.v3i1.1186 · Cited by 7

Superconducting Gravimeter Observations Show That a Satellite‐Derived Snow Depth Image Improves the Simulation of the Snow Water Equivalent Evolution in a High Alpine Site

Koch, F., Gascoin, S., Achmüller, K., Schattan, P., Wetzel, K., Deschamps‐Berger, C., Lehning, M., Rehm, T., Schulz, K., & Voigt, C. (2024) · Geophysical Research Letters · doi:10.1029/2024gl112483 · Cited by 7

MoonIndex, an Open‐Source Tool to Generate Spectral Indexes for the Moon From M3 Data

Valencia, J. E. S., Rossi, A. P., Zambon, F., Carli, C., & Nodjoumi, G. (2024) · Earth and Space Science · doi:10.1029/2023ea003464 · Cited by 7

GraphFlood 1.0: an efficient algorithm to approximate 2D hydrodynamics for landscape evolution models

Gailleton, B., Steer, P., Davy, P., Schwanghart, W., & Bernard, T. G. (2024) · Earth Surface Dynamics · doi:10.5194/esurf-12-1295-2024 · Cited by 7

Assessing Changes in Boreal Vegetation of Kola Peninsula via Large-Scale Land Cover Classification between 1985 and 2021

Sklyar, E., & Rees, G. (2022) · Remote Sensing · doi:10.3390/rs14215616 · Cited by 7

Simulating small woody feature restoration to enhance habitat connectivity in intensively farmed areas of the European Union

Marcantonio, M., Strona, G., Sedano, F., & van der Velde, M. (2024) · Landscape Ecology · doi:10.1007/s10980-024-02001-z · Cited by 6

The Application of Remote Sensing Data (SAR, Thermal and Optical) and Geodetic Modeling to Investigate the Volcanic Activity at Semeru Volcano (Indonesia)

Galetto, F., Reale, D., Coppola, D., Sansosti, E., & Pritchard, M. E. (2025) · Journal of Geophysical Research Solid Earth · doi:10.1029/2025jb031428 · Cited by 6

Two Decades of Dust Radiative Forcing on Snow Cover Across the Great Salt Lake Basin

Lang, O. I., Naple, P. W., Mallia, D. V., Hosler, T. R., Adams, B. R., & Skiles, S. M. (2025) · Journal of Geophysical Research Earth Surface · doi:10.1029/2024jf007957 · Cited by 5

Over 20,000 precolonial earthworks in the Southwest Amazonia

Pärssinen, M. H., Kalliola, R., Ranzi, A., Puttonen, E., Lopes, R. C. D. S., Virtanen, P. K., Apurinã, F., Ruokolainen, K., Hyyppä, J., Kukko, A., et al. (2026) · Nature · doi:10.1038/s41586-026-10835-7 · Cited by 5

Pysewer: A Python Library for Sewer Network Generation in Data Scarce Regions

Sanne, M., Khurelbaatar, G., Despot, D., van Afferden, M., & Friesen, J. (2024) · The Journal of Open Source Software · doi:10.21105/joss.06430 · Cited by 5

Monitoring Forest Foliar Moisture Using Sentinel-2 Reflectance and Radiative Transfer Model Inversion in Australia

Kotzur, I., Boer, M. M., Yebra, M., & Moore, B. D. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3704632 · Cited by 5

Generative adversarial networks for ensemble projections of future urban morphology

Allen-Dumas, M. R., Wheelis, A. R., Sweet-Breu, L. T., Anantharaj, J., & Kurte, K. R. (2022) · ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities (ARIC) · doi:10.1145/3557916.3567819 · Cited by 5

Estimation of Annual Productivity of Sown Rainfed Grasslands Using Machine Learning

Morais, T. G., Jongen, M., Tufik, C., Rodrigues, N. R., Gama, I., Serrano, J. M. P. R., Domingos, T., & Teixeira, R. F. M. (2025) · Grass and Forage Science · doi:10.1111/gfs.12707 · Cited by 5

Applicability evaluation and method selection in detecting cyanobacterial bloom using HY-1C/D CZI data for inland lakes

Kun, X., Ma, R., Cao, Z., Minqi, H. U., & Jiaxin, L. I. (2023) · National Remote Sensing Bulletin · doi:10.11834/jrs.20232361 · Cited by 5

Standardized Climatic Water Balance for Poland (1951–2023) Supporting Forestry and Drought Monitoring

Netzel, P., & Cywicka, D. (2025) · Scientific Data · doi:10.1038/s41597-025-06178-z · Cited by 5

Analysis of Geospatial Data Loading

Wachs, A., & Zacharatou, E. T. (2024) · International Workshop on Testing Database Systems · doi:10.1145/3662165.3662761 · Cited by 4

Forestry climate adaptation with HarvesterSeasons service—a gradient boosting model to forecast soil water index SWI from a comprehensive set of predictors in Destination Earth

Strahlendorff, M., Kröger, A., Prakasam, G., Kosmale, M., Moisander, M., Ovaskainen, H., & Poikela, A. (2024) · Frontiers in Remote Sensing · doi:10.3389/frsen.2024.1360572 · Cited by 4

The Influence of Habitat Availability, Connectivity and Limnological Conditions on Plankton and Macrophyte Biodiversity in Floodplain Lakes

Chaparro, G., Gayol, M. P., Morandeira, N. S., Kandus, P., & O’Farrell, I. (2025) · Ecohydrology · doi:10.1002/eco.70099 · Cited by 4

Exploring the potential of an Augmented Reality sandbox for geovisualization

Bärthele, R., Sauer, S., & Wilkening, J. (2023) · Proceedings of the ICA · doi:10.5194/ica-proc-5-1-2023 · Cited by 4

Modelling the spatial abundance of invasive deer and their impacts on vegetation at the landscape scale

Fedrigo, M., Bennett, A., Stewart, S. B., Forsyth, D. M., & Greet, J. (2024) · Biological Invasions · doi:10.1007/s10530-024-03282-w · Cited by 4

RDPro : Distributed Processing of Big Raster Data: [Scalable Data Science]

Shang, Z., Singla, S., Eldawy, A., & Scudiero, E. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3712221.3712229 · Cited by 4

Reactivation of the Alpine Nappe Stack During the Late‐Orogenic Collapse of the Peloponnese (Greece)

Bufféral, S., Kranis, H. D., Pubellier, M., Skourtsos, E., & Wicker, V. (2025) · Tectonics · doi:10.1029/2024tc008773 · Cited by 4

Quantifying degradation of the Imja Lake moraine dam with fused InSAR and SAR feature tracking time series

Brencher, G., Henderson, S., & Shean, D. (2026) · The Cryosphere · doi:10.5194/tc-20-67-2026 · Cited by 4

Endemic crop wild relatives in Chile towards the end of the 21st century: Protected areas and agricultural expansion

Jensen, M., & Pliscoff, P. (2023) · Crop Science · doi:10.1002/csc2.20962 · Cited by 4

Yersel referans verilerinin doğruluğunun çok zamanlı Sentinel-2 uydu görüntüleri ile araştırılması: Arpa ve Buğday örneği

YAŞAR, O., & Yağcı, A. L. (2023) · Geomatik · doi:10.29128/geomatik.1210252 · Cited by 4

Geo Engine: Workflow-driven Geospatial Portals for Data Science

Beilschmidt, C., Drönner, J., Mattig, M., & Seeger, B. (2023) · Datenbank-Spektrum · doi:10.1007/s13222-023-00453-2 · Cited by 4

Testing the reliability of soil moisture forecast for its use in agriculture

Bláhová, M., Fischer, M., Poděbradská, M., Štěpánek, P., Bálek, J., Zahradníček, P., Kudláčková, L., Žalud, Z., & Trnka, M. (2024) · Agricultural Water Management · doi:10.1016/j.agwat.2024.109073 · Cited by 3

Quantifying the functional composition and potential resilience hotspots across a large latitudinal and environmental gradient in South American forests

Deng, X., Carvajal, D. E., Urrutia‐Jalabert, R., Machida, W. S., Rosen, A., Zhang, H., Galbraith, D. R., Díaz, S. M., Malhi, Y. S., & Aguirre‐Gutiérrez, J. (2025) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2025.104704 · Cited by 3

The LUCAS dataset revisited: enhancing spatial representativeness for machine learning land cover mapping

Brodský, L., Landa, M., Bouček, T., Pešek, O., & Halounová, L. (2026) · International Journal of Digital Earth · doi:10.1080/17538947.2026.2644671 · Cited by 3

Suitability Mapping for Subsurface Floodwater Storage Schemes

Augustin, L., & Baumann, T. (2024) · InterPore Journal · doi:10.69631/ipj.v1i2nr20 · Cited by 3

Cooperative lane mapping for autonomous mobile robots in horticulture by aerial photogrammetry

Schütte, T., Dworak, V., Shamshiri, R. R., & Weltzien, C. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110515 · Cited by 3

Length, Width, and Relative Age Analysis of Lineaments in the Galileo Regional Maps with LineaMapper

Haslebacher, C., Gamazo, J., Prockter, L. M., Leonard, E. J., Rhoden, A. R., & Thomas, N. (2025) · The Planetary Science Journal · doi:10.3847/psj/add349 · Cited by 3

A least-cost network neutral landscape model of human sites and routes

Etherington, T. R., O’Sullivan, D., Perry, G. L. W., Richards, D. R., & Wainwright, J. (2024) · Landscape Ecology · doi:10.1007/s10980-024-01836-w · Cited by 3

A Parsimonious Downscaling Method for Global Potential Net Primary Production: From 30 arcmin to 30 arcsec Resolution

Weidinger, F., Matej, S., Baumbach, L., Hickler, T., Werner, C., & Erb, K. (2025) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2025jg009019 · Cited by 3

Predicting the productivity of Alpine grasslands using remote sensing informationPreprint

Vicario, S., Magnani, M., Adamo, M., Vivaldo, G., Richiardi, C., Giamberini, M., & Provenzale, A. (2024) · doi:10.5194/egusphere-2023-2824 · Cited by 3

PANGAEA Bathymetry Web Map & Feature Service - Standard Operating Procedures visualizing bathymetry data archived in PANGAEA - Data Publisher for Earth & Environmental Data

Damaske, D., Selke, N., & Balamurugan, A. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.8296986 · Cited by 3

The tortoise and the antilocaprid: adapting GPS tracking and terrain data to model wildlife walking functions

Chambers, S. N., von Nonn, J. W., Burgess, M. A., Brady, L. R., Bracewell, J., Guerra, D., & Villarreal, M. L. (2025) · Landscape Ecology · doi:10.1007/s10980-025-02092-2 · Cited by 2

Effect of Local and Landscape Variables on Bird Communities in Forest‐Agricultural Mosaics in Northeast Luzon, Philippines

Gan, J. L., Pfeifer, M., Soroño, T. C., Gicaraya, V. A. G., Torio, E., van Weerd, M., Gatan‐Balbas, M., & Shirley, M. (2025) · Biotropica · doi:10.1111/btp.70076 · Cited by 2

Geospatial machine learning libraries

Stewart, A. J., Robinson, C., & Banerjee, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-343796-0.00026-7 · Cited by 2

GIBS Geospatial Data Abstraction Library (GDAL)

(2015) · Cited by 5

The Geospatial Data Abstraction Library

Warmerdam, F. (2008) · Advances in geographic information science · doi:10.1007/978-3-540-74831-1_5 · Cited by 318

Gdal python tutorial pdf

pennwgyuv (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.12399393

GDAL: Konzept und Werkzeuge

Metz, M. (2024) · KartDok - Cartography Repository · doi:10.48711/20240627-001

vapour: Access to the 'Geospatial Data Abstraction Library' ('GDAL')

Sumner, M. (2018) · doi:10.32614/cran.package.vapour