Automatic remote sensing tools can help inform many large-scale challenges such as
disaster management, climate change, etc. While a vast amount of spatio-temporal satellite …

Machine learning methods for satellite data have a range of societally relevant applications,
but labels used to train models can be difficult or impossible to acquire. Self-supervision is a …

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 …

A key challenge for much of the machine learning work on remote sensing and earth
observation data is the difficulty in acquiring large amounts of accurately labeled data. This …

Accurate flood delineation is crucial in many disaster management tasks, such as risk map
production and update, impact estimation, claim verification, or planning of countermeasures …

Crop type maps are critical for tracking agricultural land use and estimating crop production.
Remote sensing has proven an efficient and reliable tool for creating these maps in regions …

The desired output for most real-world tasks using machine learning (ML) and remote
sensing data is a set of dense predictions that form a predicted map for a geographic region …

The advances in remote sensing technologies have boosted applications for Earth
observation. These technologies provide multiple observations or views with different levels …

Reference data is key to produce reliable crop type and cropland maps. Although research
projects, national and international programs as well as local initiatives constantly gather …

The quality of drinking water is a critical factor for public health and the environment. Inland
drinking water reservoirs are essential sources of freshwater supply for many communities …