GDAL

Monitoring Volumetric Changes in Mining Waste-Dumps Using Very High-Resolution Satellite Imagery, In-Situ GNSS Data, and SfM UAV Data Fusion

González-Pérez, I., Sanz‐Ablanedo, E., & Pérez, J. R. R. (2026) · Archives of Mining Sciences · doi:10.24425/ams.2026.157754

Forest structure from airborne laser scanning explains nationwide occurrence patterns of pine marten, stoat and least weasel across Finland

Lämsä, P., Hovi, A., Lindén, A., & Rautiainen, M. (2026) · Forest Ecology and Management · doi:10.1016/j.foreco.2026.123742

Modeling of alpine blowing snow and evaluation of the induced spatial variability

Haddjeri, A. (2024) · theses.fr (ABES) · doi:10.70675/bb397ddbz1db2z4b39z8b83zd677a041068b

Ground-based measurement techniques for snow albedo and surface temperature : enhancing satellite validation and understanding of snow processes

Arioli, S. (2024) · theses.fr (ABES) · doi:10.70675/682273ceze07ez4863z88fez06f05542891f

Ecologie des communautés des mares et étangs alpins dans un contexte de changement climatique

Lamouille‐Hébert, M. (2025) · theses.fr (ABES) · doi:10.70675/5c04dfaez6084z442cz99f2ze6c7b37c4dcc

Millora del rendiment computacional de WaterpyBal mitjançant tècniques de programació paral·lela per a l'avaluació horària de la recàrrega d'aigües subterrànies

Li, J. (2026) · UPCommons institutional repository (Universitat Politècnica de Catalunya)

MyLiDAR: Improving LiDAR data processing and analysis with a comprehensive open-source QGIS plugin

Fernández-González, P., Cuartero, A., Broncano, F., & Rodríguez, P. G. (2026) · Earth Science Informatics · doi:10.1007/s12145-026-02126-6

A SAR and optical remote sensing dataset in Seville for scientific research in agriculture

Villarroya-Carpio, A., Cazcarra-Bes, V., Mestre-Quereda, A., & Lopez-Sanchez, J. M. (2026) · Scientific Data · doi:10.1038/s41597-026-07419-5

Europe wide agricultural land use systems by harmonizing the Copernicus High Resolution Layers on grassland and croplands

Fahl, F., van der Velde, M., Claverie, M., Yordanov, M., Sánchez, L. M., & Lugato, E. (2026) · Scientific Data · doi:10.1038/s41597-026-07374-1

Soil Carbon Stocks in Fijian Seagrass: Environmental Drivers, National Inventories, and Implications for Blue Carbon Accounting in Small Island Settings

Crosswell, J. R., Powell, W.-J., Carlin, G., Asanopoulos, C. H., Wentzel, C. E., Lyons, M., & Steven, A. (2026) · Global Biogeochemical Cycles · doi:10.1029/2026gb009079

Machine learning-driven, near-surface velocity modelling in the North Sea: using onshore data to predict offshore conditions

Sanger, M. D., Carlton, B. D., Liu, Z., Vanneste, M., Griffiths, L., & Maurer, B. W. (2026) · Georisk Assessment and Management of Risk for Engineered Systems and Geohazards · doi:10.1080/17499518.2026.2676033

Predicting the risk of individual tree fall along powerlines in Norway with a mechanistic wind risk model and machine learning

Merlin, M., Gardiner, B., & Solberg, S. (2026) · Natural hazards and earth system sciences · doi:10.5194/nhess-26-2461-2026

Early hyperspectral detection of Carlavirus vignae in common bean under field conditionsPreprint

Ribeiro, W. R., da Silva, J. A. F., Almeida, A. D. S., Ferreira, A. L., Silva, R. M., Alves, T. M., Geraldine, A. M., Pinheiro, P. V., & Lobo, M. (2026) · Research Square · doi:10.21203/rs.3.rs-9694653/v1

Mapping understorey tree invasion in a drought-affected forest using multi-view UAV imagery and deep learning

Dogotari, M., Kleinebecker, T., Becker, R., & Große‐Stoltenberg, A. (2026) · ISPRS Open Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.ophoto.2026.100133

Benchmarking Spatial Operations over Heterogeneous Data: The Case of Zonal Statistics

Dusella, G., Gavriilidis, H., Markl, V., & Zacharatou, E. T. (2026) · International Workshop on Testing Database Systems · doi:10.1145/3810991.3811631

Comment on egusphere-2026-2858

(2026) · doi:10.5194/egusphere-2026-2858-rc1

JupyterGIS: A Collaborative GIS Environment for JupyterLab

Renou, M., Verma, A., Fisher, M., Mooney, G., Meschede, M., Brichet, N., Brochart, D., Verma, N., Fouilloux, A., Corlay, S., et al. (2026) · The Journal of Open Source Software · doi:10.21105/joss.10383

GLORIF1, a global river flow dataset created by integrating process-based modelling and machine learning

Işık, S. B., Moya, O. P., Magni, M., Nussbaum, M., Karssenberg, D., & Sutanudjaja, E. H. (2026) · Scientific Data · doi:10.1038/s41597-026-07658-6

ETHOS.GeoKit: A Python toolkit for analyzing and altering geospatial data for energy systems modeling and beyond

Ishmam, S., Belina, J., Winkler, C., Weinand, J. M., Pflugradt, N., Heinrichs, H., & Linßen, J. (2026) · SoftwareX · doi:10.1016/j.softx.2026.102870

Seismic evidence of multi-stage volcanic flank collapse and refined deposit volume offshore Fogo, Cabo Verde

Hadré, E., Lebas, É., Ramalho, R. S., Grob, H., Quartau, R., Barrett, R., Scheffler, J. M., Klaucke, I., Kutterolf, S., & Krastel, S. (2026) · Bulletin of Volcanology · doi:10.1007/s00445-026-02014-3

Consistent and Harmonized Forest Statistics for Forest-use Intensity Analyses Across Europe (2000–2023)Preprint

Weidinger, F., Matej, S., Egger, C., Gingrich, S., Rein, L., Lerchbaum, E., Herold, M., Runge, A., Fulvio, F. D., See, L., et al. (2026) · Earth System Science Data Discussions · doi:10.5194/essd-2026-540

Wildfire Hazard in Germany Under Worst-Case ConditionsPreprint

Heisig, J., & Pebesma, E. J. (2026) · doi:10.5194/egusphere-2026-4416

Spatial Variability and Drivers of Salt Marsh Cliff Erosion in the Dutch Wadden Sea: From Observation to Simulation

Dzimballa, S., Kitsikoudis, V., Willemsen, P. W. J. M., Folmer, E. O., Borsje, B. W., Georgiou, I. Y., Bregman, M., & Augustijn, D. C. M. (2026) · Journal of Geophysical Research Earth Surface · doi:10.1029/2025jf008855

Machine Learning Approaches for Temporal Classification of Forest and Shrub Cover in Burned Landscapes in Mediterranean Ecosystems

Iranzo, C., Pérez, F., & Alves, D. B. (2026) · PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · doi:10.1007/s41064-026-00415-9

Pixels and Patterns in the Sand: Decoding Coastal Dune Habitats With Multi‐Resolution CNNs

Pafumi, E., Angiolini, C., Bacaro, G., de Simone, L., Fanfarillo, E., Fiaschi, T., Rocchini, D., Thouverai, E., & Maccherini, S. (2026) · Journal of Vegetation Science · doi:10.1111/jvs.70173

A Hybrid State-Space Approach for Census-Tract Population EstimationPreprint

Ye, J. R., & Morozov, A. V. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2608.30094

Creation of a detailed land use land cover product for the territory of the European Union based on OpenStreetMap and CLCplus data

Bouček, T., Landa, M., Brodský, L., Halounová, L., & Pešek, O. (2026) · Geocarto International · doi:10.1080/10106049.2026.2720980

A cloud-native, compute-optimized tile grid for land cover analysis

Maschler, T., Mccarthy, R., & Anderson, C. B. (2026) · Stacks. · doi:10.60102/stacks-26010

GeoWombat: Scalable geospatial and remote sensing analysis in Python

Graesser, J., Mann, M. L., Hardtke, L., Denham, R. J., & Xu, S. (2026) · The Journal of Open Source Software · doi:10.21105/joss.10812

OpenLiDARViewer

Urias, A. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.22730128

Long-Term Drought Variability in Southwestern Uganda: A Comparative Analysis of Ground and Satellite Rainfall Data for Mbarara

Mutabazi, T., Nuwagira, U., Habyarimana, V., Mubiru, D., Muheki, P., Ntegyerize, Y., Jackline, K., & Munyarugero, E. (2026) · East African Journal of Environment and Natural Resources · doi:10.37284/eajenr.9.3.5790

FotoFácil: Uma Plataforma Visual para Processamento de Imagens e Preparação de Dados para Inteligência Artificial

de Oliveira, N. H. G., & Shiguemori, A. P. A. D. C. E. (2026) · Biblioteca Digital da Memória Científica do INPE (National Institute for Space Research) · doi:10.5281/zenodo.22983976

GeoRacoon: A Python package for scalable, parallelized geospatial raster analysis

Landauer, S., Niklaus, P. A., & Liechti, J. I. (2026) · The Journal of Open Source Software · doi:10.21105/joss.10797

Meteorological forecasting of respiratory diseases incidence with geospatial and machine learning methods

Arroquia-Cuadros, B., Marqués‐Mateu, Á., Fdez-Arróyabe, P., & Sebastiá, L. (2026) · Scientific Reports · doi:10.1038/s41598-026-74933-2

A dataset of 538 high-resolution ecogeographical variables for species distribution modelling and conservation planning, describing Latvia in 2024Preprint

Avotiņš, A., Vinogradovs, I., Butkeviča, J., Rubene, B., Rūsiņa, S., Starka, R., Šakele, V., Vērpēja, V., & Auniņš, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7642360

Desarrollo de herramientas de automatización para la optimización del SIG corporativo de Basotik Fundazioa en la gestión forestal de Gipuzkoa

Ortiz, L. S. (2026) · Academica-e (Universidad Pública de Navarra)

Canadian fractional landcover maps from 1985 onward

Guindon, L., Correia, D. L. P., Gahrouei, O. R., Perbet, P., Smiley, B. P., Stralberg, D., Collins, L., Parisien, M., Wang, X., Manka, F., et al. (2026) · Scientific Data · doi:10.1038/s41597-026-08522-3

Where the Water Begins: A Field Guide to Six Streams Rising in the Knuckles Mountain Range, Sri LankaPreprint

Chamika, K. H. T. (2026) · doi:10.31223/x5k51s

In-field hyperspectral detection of Potato virus Y, Potato leafroll virus and blackleg in seed potato canopies using an autonomous ground robot

Argüello, D., Afonso, M. V., Aquino, A., Noguera, M. Á. D., Lugtenburg, C., & Polder, G. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.102689

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