| Supervised and semi-supervised self-organizing maps for regression and classification focusing on hyperspectral data FM Riese, S Keller, S Hinz Remote Sensing 12 (1), 7, 2019 | 95 | 2019 |
| Deep learning for land cover change detection O Sefrin, FM Riese, S Keller Remote Sensing 13 (1), 78, 2020 | 90 | 2020 |
| Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity S Keller, PM Maier, FM Riese, S Norra, A Holbach, N Börsig, A Wilhelms, ... International journal of environmental research and public health 15 (9), 1881, 2018 | 78 | 2018 |
| Soil texture classification with 1D convolutional neural networks based on hyperspectral data FM Riese, S Keller arXiv preprint arXiv:1901.04846, 2019 | 63 | 2019 |
| Mapping natural hazard impacts on road infrastructure—the extreme precipitation in Baden-Württemberg, Germany, June 2013 S Keller, A Atzl International Journal of Disaster Risk Science 5, 227-241, 2014 | 38 | 2014 |
| Introducing a framework of self-organizing maps for regression of soil moisture with hyperspectral data FM Riese, S Keller IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium …, 2018 | 34 | 2018 |
| Advancing Ground-Based Radar Processing for Bridge Infrastructure Monitoring C Michel, S Keller Sensors 21 (6), 2172, 2021 | 29 | 2021 |
| Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies PM Maier, S Keller, S Hinz Remote Sensing 13 (4), 718, 2021 | 28 | 2021 |
| The contribution of tsunami evacuation analysis to evacuation planning in Chile: Applying a multi-perspective research design S Kubisch, J Guth, S Keller, MT Bull, L Keller, AC Braun International journal of disaster risk reduction 45, 101462, 2020 | 27 | 2020 |
| Machine learning regression on hyperspectral data to estimate multiple water parameters PM Maier, S Keller 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in …, 2018 | 24 | 2018 |
| Supervised Machine Learning Approaches on Multispectral Remote Sensing Data for a Combined Detection of Fire and Burned Area. Remote Sens. 2022, 14, 657 J Florath, S Keller Remote sensing, 2022 | 22 | 2022 |
| Application of different simulated spectral data and machine learning to estimate the chlorophyll a concentration of several inland waters PM Maier, S Keller 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution …, 2019 | 22 | 2019 |
| Supervised, semi-supervised, and unsupervised learning for hyperspectral regression FM Riese, S Keller Hyperspectral image analysis: Advances in machine learning and signal …, 2020 | 21 | 2020 |
| Investigation of the impact of dimensionality reduction and feature selection on the classification of hyperspectral EnMAP data S Keller, AC Braun, S Hinz, M Weinmann th Workshop on Hyperspectral Image and Signal Processing: Evolution in …, 2016 | 20 | 2016 |
| Unsupervised feature selection based on ultrametricity and sparse training data: A case study for the classification of high-dimensional hyperspectral data PE Bradley, S Keller, M Weinmann Remote Sensing 10 (10), 1564, 2018 | 19 | 2018 |
| Estimating chlorophyll a concentrations of several inland waters with hyperspectral data and machine learning models PM Maier, S Keller arXiv preprint arXiv:1904.02052, 2019 | 18 | 2019 |
| Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data S Keller, FM Riese, J Stötzer, PM Maier, S Hinz arXiv preprint arXiv:1804.09046, 2018 | 18 | 2018 |
| Development of a generic concept to analyze the accessibility of emergency facilities in critical road infrastructure for disaster scenarios: exemplary application for the 2017 … J Guth, S Wursthorn, AC Braun, S Keller Natural Hazards 97, 979-999, 2019 | 16 | 2019 |
| Machine learning framework for the estimation of average speed in rural road networks with openstreetmap data S Keller, R Gabriel, J Guth ISPRS international journal of geo-information 9 (11), 638, 2020 | 15 | 2020 |
| Combining a social science approach and GIS-based simulation to analyse evacuation in natural disasters: A case study in the Chilean community of Talcahuano. S Kubisch, J Stötzer, S Keller, MT Bull, A Braun ISCRAM, 2019 | 11 | 2019 |
Felix M. RieseResearcher, Karlsruhe Institute of TechnologyVerified email at kit.edu
