A large-scale climate-aware satellite image dataset for domain adaptive land-cover semantic segmentation
In this paper, we present a unique large-scale Climate-Aware Satellite Images Dataset (CASID) specifically designed for domain adaptive land-cover semantic segmentation.
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Key points
- A few well-annotated datasets for land-cover semantic segmentation have recently been introduced to advance the field of earth observation technologies.
- To enhance land-cover semantic segmentation with improved generalization ability, we conducted the first investigation into the impact of climate on this task.
- This characteristic makes CASID the first climate-aware land-cover semantic segmentation dataset with multiple domains.
- Moreover, we conduct extensive experiments to evaluate the latest semantic segmentation and unsupervised domain adaptation methods on the CASID dataset.
Sources (1)
- [1]A large-scale climate-aware satellite image dataset for domain adaptive land-cover semantic segmentationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 04:36 AM
In this paper, we present a unique large-scale Climate-Aware Satellite Images Dataset (CASID) specifically designed for domain adaptive land-cover semantic segmentation.
A few well-annotated datasets for land-cover semantic segmentation have recently been introduced to advance the field of earth observation technologies.
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