We prepare 1K real-world wildfire scenarios for testing the AI models. We also propose a deep transformer, S2R-FireTr, which excels in considering the multi-modal environmental information for forecasting and backtracking the wildfire.

Satellite imagery is increasingly available, high resolution, and temporally detailed. Changes in spatio-temporal datasets such as satellite images are particularly interesting as they reveal the many events and forces that shape our world. However, finding such interesting and meaningful change events from the vast data is challenging. In this paper, we present new datasets for such change events that include semantically meaningful events like road construction. Instead of manually annotating the very large corpus of satellite images, we introduce a novel unsupervised approach that takes a large spatio-temporal dataset from satellite images and finds interesting change events. To evaluate the meaningfulness on these datasets we create 2 benchmarks namely CaiRoad and CalFire which capture the events of road construction and forest fires. These new benchmarks can be used to evaluate semantic retrieval/classification performance. We explore these benchmarks qualitatively and quantitatively by using several methods and show that these new datasets are indeed challenging for many existing methods.

"We really can't do this research without collaboration." Wąsala collaborates with atmospheric scientists from SRON (Space Research Organisation Netherlands) on machine learning models that detect large greenhouse gas emissions from space. There is too much data to review manually, and such models offer a solution. How much greenhouse gas do humans emit? The machine learning method Wąsala refers to detects emissions in the form of a point source: plumes.

Models (SDMs) predict species across space from spatially explicit features.

Our insight is that satellite's earth observation photos are not just images

We perform extensive experiments to confirm the effectiveness of AnyChange for zero-shot change detection.

Although it is promising, parsing fine-grained road structures from satellite images for map construction is a highly challenging task for the following reasons.