MapInWild: A remote sensing dataset to address the question of what makes nature wild [Software and Data Sets]
Author:
Affiliation:
1. Institute of Space Technology and Space Applications, Department of Aerospace Engineering, University of the Bundeswehr Munich, Neubiberg, Germany
2. Institute of Geodesy and Geoinformation, University of Bonn, Bonn, Germany
Funder
DFG
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,General Earth and Planetary Sciences,Instrumentation,General Computer Science
Link
http://xplorestaging.ieee.org/ielx7/6245518/10089566/10089830.pdf?arnumber=10089830
Reference34 articles.
1. Multitask Learning for Human Settlement Extent Regression and Local Climate Zone Classification
2. When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs
3. ResNeSt: Split-attention networks;zhang,2020
4. Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
5. Perturbation-Seeking Generative Adversarial Networks: A Defense Framework for Remote Sensing Image Scene Classification
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1. Multi-Scale Context Fusion for Pixel-Level Naturalness Mapping Using Sentinel-2 Imagery;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07
2. Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery;IEEE Geoscience and Remote Sensing Letters;2024
3. CaBuAr: California burned areas dataset for delineation [Software and Data Sets];IEEE Geoscience and Remote Sensing Magazine;2023-09
4. Explaining Multimodal Data Fusion: Occlusion Analysis for Wilderness Mapping;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16
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