Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Schneider, Maja"'
We propose an approach for early crop classification through identifying important timesteps with eXplainable AI (XAI) methods. Our approach consists of training a baseline crop classification model to carry out layer-wise relevance propagation (LRP)
Externí odkaz:
http://arxiv.org/abs/2310.06574
Autor:
Schneider, Maja, Körner, Marco
Publikováno v:
IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2022
With leaps in machine learning techniques and their applicationon Earth observation challenges has unlocked unprecedented performance across the domain. While the further development of these methods was previously limited by the availability and vol
Externí odkaz:
http://arxiv.org/abs/2310.06393
Accurate in-season crop type classification is crucial for the crop production estimation and monitoring of agricultural parcels. However, the complexity of the plant growth patterns and their spatio-temporal variability present significant challenge
Externí odkaz:
http://arxiv.org/abs/2305.12011
EuroCrops contains geo-referenced polygons of agricultural croplands from 16 countries of the European Union (EU) as well as information on the respective crop species grown there. These semantic annotations are derived from self-declarations by farm
Externí odkaz:
http://arxiv.org/abs/2302.10202
Publikováno v:
Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT 2023
Machine learning (ML) can help fight pandemics like COVID-19 by enabling rapid screening of large volumes of images. To perform data analysis while maintaining patient privacy, we create ML models that satisfy Differential Privacy (DP). Previous work
Externí odkaz:
http://arxiv.org/abs/2211.11434
Publikováno v:
Workshop on Broadening Research Collaborations in ML (NeurIPS 2022)
Expansive, informative datasets are vital in providing foundations and possibilities for scientific research and development across many fields of study. Assembly of grand datasets, however, frequently poses difficulty for the author and stakeholders
Externí odkaz:
http://arxiv.org/abs/2210.07178
Publikováno v:
In Geoderma Regional September 2024 38
Publikováno v:
Proc. of the 2021 conference on Big Data from Space (BiDS21), 2021, 5, 125-128
We present EuroCrops, a dataset based on self-declared field annotations for training and evaluating methods for crop type classification and mapping, together with its process of acquisition and harmonisation. By this, we aim to enrich the research
Externí odkaz:
http://arxiv.org/abs/2106.08151
Publikováno v:
In Remote Sensing of Environment 1 May 2024 305
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