A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat M Witjes, L Parente, CJ van Diemen, T Hengl, M Landa, L Brodský, ... PeerJ 10, e13573, 2022 | 28 | 2022 |
Linked Spatial Data for a Circular Economy: Exploring its potential through a Textile Use Case. E Sauter, M Witjes SEMANTiCS (Posters & Demos), 2017 | 17 | 2017 |
sOilFauna-a global synthesis effort on the drivers of soil macrofauna communities and functioning J Mathieu, AC Antunes, S Barot, AE Bonato Asato, MLC Bartz, GG Brown, ... | 13 | 2022 |
Ecodatacube.eu: Analysis-ready open environmental data cube for Europe M Witjes, L Parente, J Križan, T Hengl, L Antonić PeerJ 11, e15478, 2023 | 9 | 2023 |
Continental Europe land cover mapping at 30m resolution based CORINE and LUCAS on samples L Parente, M Witjes, T Hengl, M Landa, L Brodsky Zenodo. Org. https://doi. org/10, 2021 | 7 | 2021 |
Time-series of Landsat-based bi-monthly and annual spectral indices for continental Europe for 2000–2022 X Tian, D Consoli, M Witjes, F Schneider, L Parente, M Şahin, YF Ho, ... Earth System Science Data Discussions 2024, 1-49, 2024 | 3 | 2024 |
A computational framework for processing time-series of Earth Observation data based on discrete convolution: global-scale historical Landsat cloud-free aggregates at 30 m … D Consoli, L Parente, R Simoes, M Şahin, X Tian, M Witjes, L Sloat, ... | 2 | 2024 |
Iterative mapping of probabilities: A data fusion framework for generating accurate land cover maps that match area statistics M Witjes, M Herold, S de Bruin International Journal of Applied Earth Observation and Geoinformation 131 …, 2024 | 1 | 2024 |
Global Pasture Watch-Global machine learning model for prediction of cultivated and natural/semi-natural grassland L Parente, L Sloat, V Mesquita, D Consoli, R Stanimirova, T Hengl, ... Zenodo, 2024 | 1 | 2024 |
Global Pasture Watch-Annual grassland class and extent maps at 30-m spatial resolution (2000—2022) L Parente, L Sloat, V Mesquita, D Consoli, R Stanimirova, T Hengl, ... Zenodo, 2024 | 1 | 2024 |
Annual 30-m maps of global grassland class and extent (2000–2022) based on spatiotemporal Machine Learning L Parente, L Sloat, V Mesquita, D Consoli, R Stanimirova, T Hengl, ... Scientific data 11 (1), 1-22, 2024 | | 2024 |
Mapping global grassland dynamics 2000—2022 at 30m spatial resolution using spatiotemporal Machine Learning L Parente, L Sloat, V Mesquita, D Consoli, R Stanimirova, T Hengl, ... | | 2024 |
A new methodology for time-series reconstruction of global scale historical Earth observation data D Consoli, L Parente, M Witjes EGU24, 2024 | | 2024 |
Mapping everything, everywhere, all the time: Modeling European land cover using data fusion and machine learning M Witjes PQDT-Global, 2024 | | 2024 |
A harmonized Landsat Sentinel-2 (HLS) dataset for benchmarking time series reconstruction methods of vegetation indices D Consoli, L Leal Parente, M Witjes, T Hengl OpenGeoHub Foundation, 2023 | | 2023 |
Iterative Mapping of Probabilities M Witjes, M Herold, S de Bruin Wageningen University & Research, 0 | | |