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Recognizing protected and anthropogenic patterns in landscapes using interpretable machine learning and satellite imagery., , , and . Frontiers Artif. Intell., (February 2023)Behind the Leaves: Estimation of Occluded Grapevine Berries With Conditional Generative Adversarial Networks., , , , , and . Frontiers Artif. Intell., (2022)Location-aware Adaptive Denormalization: A Deep Learning Approach For Wildfire Danger Forecasting., , and . CoRR, (2022)Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks., , , and . CoRR, (2021)Time Dependent Image Generation of Plants from Incomplete Sequences with CNN-Transformer., , , and . GCPR, volume 13485 of Lecture Notes in Computer Science, page 495-510. Springer, (2022)ArtifiVe-Potsdam: A Benchmark for Learning with Artificial Objects for Improved Aerial Vehicle Detection., , and . IGARSS, page 1214-1217. IEEE, (2021)A Whale's Tail - Finding the Right Whale in an Uncertain World., , , , and . xxAI@ICML, volume 13200 of Lecture Notes in Computer Science, page 297-313. Springer, (2020)Detection of Anomalous Grapevine Berries Using All-Convolutional Autoencoders., , and . IGARSS, page 3701-3704. IEEE, (2019)Explainable Machine Learning., and . Mach. Learn. Knowl. Extr., 5 (1): 169-170 (March 2023)Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery., , and . IEEE Geosci. Remote. Sens. Lett., (2024)