تقرير
GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images
| العنوان: | GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images |
|---|---|
| المؤلفون: | Crall, Jon, Greenwell, Connor, Joy, David, Leotta, Matthew, Chaudhary, Aashish, Hoogs, Anthony |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Learning from multiple sensors is challenging due to spatio-temporal misalignment and differences in resolution and captured spectra. To that end, we introduce GeoWATCH, a flexible framework for training models on long sequences of satellite images sourced from multiple sensor platforms, which is designed to handle image classification, activity recognition, object detection, or object tracking tasks. Our system includes a novel partial weight loading mechanism based on sub-graph isomorphism which allows for continually training and modifying a network over many training cycles. This has allowed us to train a lineage of models over a long period of time, which we have observed has improved performance as we adjust configurations while maintaining a core backbone. Comment: IGARSS 2024 https://2024.ieeeigarss.org/view_paper.php?PaperNum=5431 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.06337 |
| رقم الأكسشن: | edsarx.2407.06337 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |