| ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data FI Diakogiannis, F Waldner, P Caccetta, C Wu ISPRS Journal of Photogrammetry and Remote Sensing 162, 94-114, 2020 | 1032 | 2020 |
| The academic, economic and societal impacts of Open Access: an evidence-based review JP Tennant, F Waldner, DC Jacques, P Masuzzo, LB Collister, ... F1000Research 5, 2016 | 584 | 2016 |
| A comparison of global agricultural monitoring systems and current gaps S Fritz, L See, JCL Bayas, F Waldner, D Jacques, I Becker-Reshef, ... Agricultural systems 168, 258-272, 2019 | 303 | 2019 |
| A multi-disciplinary perspective on emergent and future innovations in peer review JP Tennant, JM Dugan, D Graziotin, DC Jacques, F Waldner, D Mietchen, ... F1000Research 6, 2017 | 286 | 2017 |
| Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods E Kamir, F Waldner, Z Hochman ISPRS Journal of Photogrammetry and Remote Sensing 160, 124-135, 2020 | 212 | 2020 |
| Sentinel-2’s potential for sub-pixel landscape feature detection J Radoux, G Chomé, DC Jacques, F Waldner, N Bellemans, N Matton, ... Remote Sensing 8 (6), 488, 2016 | 200 | 2016 |
| Automated annual cropland mapping using knowledge-based temporal features F Waldner, GS Canto, P Defourny ISPRS Journal of Photogrammetry and Remote Sensing 110, 1-13, 2015 | 198 | 2015 |
| Deep learning on edge: Extracting field boundaries from satellite images with a convolutional neural network F Waldner, FI Diakogiannis Remote sensing of environment 245, 111741, 2020 | 177 | 2020 |
| An automated method for annual cropland mapping along the season for various globally-distributed agrosystems using high spatial and temporal resolution time series N Matton, G Sepulcre Canto, F Waldner, S Valero, D Morin, J Inglada, ... Remote Sensing 7 (10), 13208-13232, 2015 | 172 | 2015 |
| Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity F Waldner, D De Abelleyra, SR Verón, M Zhang, B Wu, D Plotnikov, ... International Journal of Remote Sensing 37 (14), 3196-3231, 2016 | 128 | 2016 |
| Mapping priorities to focus cropland mapping activities: Fitness assessment of existing global, regional and national cropland maps F Waldner, S Fritz, A Di Gregorio, P Defourny Remote Sensing 7 (6), 7959-7986, 2015 | 121 | 2015 |
| Mapping cropland abandonment in the Aral Sea Basin with MODIS time series F Löw, AV Prishchepov, F Waldner, O Dubovyk, A Akramkhanov, ... Remote Sensing 10 (2), 159, 2018 | 92 | 2018 |
| A generic algorithm to estimate LAI, FAPAR and FCOVER variables from SPOT4_HRVIR and Landsat sensors: Evaluation of the consistency and comparison with ground measurements W Li, M Weiss, F Waldner, P Defourny, V Demarez, D Morin, O Hagolle, ... Remote Sensing 7 (11), 15494-15516, 2015 | 85 | 2015 |
| Cropland mapping over Sahelian and Sudanian agrosystems: A Knowledge-based approach using PROBA-V time series at 100-m MJ Lambert, F Waldner, P Defourny Remote Sensing 8 (3), 232, 2016 | 75 | 2016 |
| Maize leaf area index retrieval from synthetic quad pol SAR time series using the water cloud model E Bériaux, F Waldner, F Collienne, P Bogaert, P Defourny Remote Sensing 7 (12), 16204-16225, 2015 | 70 | 2015 |
| Land cover and crop type classification along the season based on biophysical variables retrieved from multi-sensor high-resolution time series F Waldner, MJ Lambert, W Li, M Weiss, V Demarez, D Morin, ... Remote Sensing 7 (8), 10400-10424, 2015 | 68 | 2015 |
| A unified cropland layer at 250 m for global agriculture monitoring F Waldner, S Fritz, A Di Gregorio, D Plotnikov, S Bartalev, N Kussul, ... Data 1 (1), 3, 2016 | 64 | 2016 |
| A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform JC Laso Bayas, M Lesiv, F Waldner, A Schucknecht, M Duerauer, L See, ... Scientific data 4 (1), 1-10, 2017 | 63 | 2017 |
| Needle in a haystack: Mapping rare and infrequent crops using satellite imagery and data balancing methods F Waldner, Y Chen, R Lawes, Z Hochman Remote Sensing of Environment 233, 111375, 2019 | 62 | 2019 |
| Yield forecasting with machine learning and small data: What gains for grains? M Meroni, F Waldner, L Seguini, H Kerdiles, F Rembold Agricultural and Forest Meteorology 308, 108555, 2021 | 61 | 2021 |
Pierre Defournyprofesseur, université catholique de LouvainVerified email at uclouvain.be
