Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Nuske, Marlon"'
The availability of temporal geospatial data in multiple modalities has been extensively leveraged to enhance the performance of machine learning models. While efforts on the design of adequate model architectures are approaching a level of saturatio
Externí odkaz:
http://arxiv.org/abs/2408.11384
Autor:
Venkatesh, Supreeth Mysore, Macaluso, Antonio, Nuske, Marlon, Klusch, Matthias, Dengel, Andreas
The increasing number of Low Earth Orbit (LEO) satellites, driven by lower manufacturing and launch costs, is proving invaluable for Earth observation missions and low-latency internet connectivity. However, as the number of satellites increases, the
Externí odkaz:
http://arxiv.org/abs/2408.06007
Vegetation indices allow to efficiently monitor vegetation growth and agricultural activities. Previous generations of satellites were capturing a limited number of spectral bands, and a few expert-designed vegetation indices were sufficient to harne
Externí odkaz:
http://arxiv.org/abs/2407.08298
Crop yield forecasting plays a significant role in addressing growing concerns about food security and guiding decision-making for policymakers and farmers. When deep learning is employed, understanding the learning and decision-making processes of t
Externí odkaz:
http://arxiv.org/abs/2407.08274
Since the launch of the Sentinel-2 (S2) satellites, many ML models have used the data for diverse applications. The scene classification layer (SCL) inside the S2 product provides rich information for training, such as filtering images with high clou
Externí odkaz:
http://arxiv.org/abs/2406.18584
Autor:
Venkatesh, Supreeth Mysore, Macaluso, Antonio, Nuske, Marlon, Klusch, Matthias, Dengel, Andreas
Quantum computing is expected to transform a range of computational tasks beyond the reach of classical algorithms. In this work, we examine the application of variational quantum algorithms (VQAs) for unsupervised image segmentation to partition ima
Externí odkaz:
http://arxiv.org/abs/2405.14405
Earth observation (EO) applications involving complex and heterogeneous data sources are commonly approached with machine learning models. However, there is a common assumption that data sources will be persistently available. Different situations co
Externí odkaz:
http://arxiv.org/abs/2403.14297
Autor:
Mena, Francisco, Pathak, Deepak, Najjar, Hiba, Sanchez, Cristhian, Helber, Patrick, Bischke, Benjamin, Habelitz, Peter, Miranda, Miro, Siddamsetty, Jayanth, Nuske, Marlon, Charfuelan, Marcela, Arenas, Diego, Vollmer, Michaela, Dengel, Andreas
Accurate crop yield prediction is of utmost importance for informed decision-making in agriculture, aiding farmers, and industry stakeholders. However, this task is complex and depends on multiple factors, such as environmental conditions, soil prope
Externí odkaz:
http://arxiv.org/abs/2401.11844
Autor:
Venkatesh, Supreeth Mysore, Macaluso, Antonio, Nuske, Marlon, Klusch, Matthias, Dengel, Andreas
We present Q-Seg, a novel unsupervised image segmentation method based on quantum annealing, tailored for existing quantum hardware. We formulate the pixel-wise segmentation problem, which assimilates spectral and spatial information of the image, as
Externí odkaz:
http://arxiv.org/abs/2311.12912
Autor:
Pathak, Deepak, Miranda, Miro, Mena, Francisco, Sanchez, Cristhian, Helber, Patrick, Bischke, Benjamin, Habelitz, Peter, Najjar, Hiba, Siddamsetty, Jayanth, Arenas, Diego, Vollmer, Michaela, Charfuelan, Marcela, Nuske, Marlon, Dengel, Andreas
We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions. We use high-resolution crop yield maps as ground truth data to train crop and ma
Externí odkaz:
http://arxiv.org/abs/2308.08948