Dynamic land use mapping using the collective power oft he crowd

Traditional land use and land cover (LULC) mapping has long relied strongly on input from Earth Observation (EO) data sources at various resolutions and scale levels. With high performance and cloud computing on the rise, rapid processing of large volumes of very high resolution (VHR) satellite imagery-big EO data- is becoming less problematic. Consequently, scientific challenges in that topical domain move on to the next level. "Remote Sensing Science 2.0" has been coined as having a primary focus on the advanced consideration of temporal scale, i.e. change and dynamics, in addition to the traditional spatial aspects (Herold 2011). The recent emergence of EO satellite constellations (some already operational, some in the planning stage) set up in temporally shifted coplanar orbits (e.g. ESA´s Sentinels, Airbus´ Pléiades, Planet´s RapidEye, UrtheCast´s OptiSAR) as well as smallsat and nanosat swarms (e.g. Planet´s Doves, BlackSky´s Pathfinders, Terra Bella´s SkySats) comes in line with increased awareness and efforts to tackle the space-time resolution dichotomy of traditional space-based Earth Observation and related analytics.