Our Environment is perpetually subject to changes in space and time with significantly varying triggers, frequencies, magnitudes and also consequences to humans.

It is critical to monitor Earth surface processes (e.g. coastal erosion, surface deformation, land cover changes) and natural ecosystem to improve our scientific understanding and knowledge of complex human-­‐environmental interactions. Understanding is key and the first step to informed decision making (e.g. adaptation, mitigation).

Recently, we can observe an increasing proliferation of heterogeneous geospatial data (point cloud data, aerial or terrestrial videos and photographs, very high spatial resolution –VHSR and stereoscopic satellite images), acquired with very-­‐ high temporal frequency (VHTF) by various platforms and sensors. This includes in particular terrestrial laser scanners (TLS), mobile mapping system (MMS), terrestrial photo cameras, aerial platforms with optical cameras and laser scanners

(e.g. Unmanned Aerial Vehicles – UAVs or satellites (e.g. Pléiades, Sentinel-­‐1/2)) . In addition, large historical archives of geospatial datasets from previous sensor generations exist, and while they constitute an invaluable source of information for the analysis of historical changes, they also further contribute to the data deluge.

These systems can rapidly deliver massive heterogeneous geospatial data for environmental mapping and monitoring. Although a multitude of automatic methods were developed to extract environmental parameters (e.g. extent, volumes, velocities, typology) only from LiDAR point or only from aerial or satellite images very little research has focused on environmental mapping and monitoring combining multisource heterogeneous geospatial data from VHTF and VHSR platforms and sensors.

Within this context, the objectives of the TIMES project is to produce new knowledge on the dynamics landscape objects from the massive exploitation of this big geospatial data with the objective to develop and validate novel data processing and analysis methods for environmental monitoring of landscape objects. The proposed methods will be able to tackle highly heterogeneous datasets (point cloud data, aerial and satellite images) analyzed at very high temporal frequency.