The digital transformation taking place in all areas of life has led to a massive increase in digital datain particular, related to the places where and the ways how we live. To facilitate an exploration of the new opportunities arising from this development the Urban Thematic Exploitation Platform (U-TEP) has been set-up. This enabling instrument represents a virtual environment that combines open access to multisource data repositories with dedicated data processing, analysis and visualisation functionalities. Moreover, it includes mechanisms for the development and sharing of technology and knowledge. After an introduction of the underlying methodical concept, this paper introduces four selected use cases that were carried out on the basis of U-TEP: two technology-driven applications implemented by users from the remote sensing and software engineering community (generation of cloud-free mosaics, processing of drone data) and two examples related to concrete use scenarios defined by planners and decision makers (data analytics related to global urbanization, monitoring of regional land-use dynamics). The experiences from U-TEP's pre-operations phase show that the system can effectively support the derivation of new data, facts and empirical evidence that helps scientists and decision-makers to implement improved strategies for sustainable urban development.

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On August 14, 2021, a Mw 7.2 earthquake struck the Tiburon Peninsula of western Haiti triggering thousands of landslides. Three days after the earthquake on August 17, 2021, Tropical Storm Grace crossed shallow waters offshore of southern Haiti triggering more landslides worsening the situation. In the aftermath of these events, several organizations with disaster response capabilities or programs activated to provide information on the location of landslides to first responders on the ground. Utilizing remote sensing to support rapid response, one organization manually mapped initiation point of landslides and three automatically detected landslides. The 2021 Haiti event also provided a unique opportunity to test different automated landslide detection methods that utilized both SAR and optical data in a rapid response scenario where rapid situational awareness was critical. As the methods used are highly replicable, the main goal of this study is to summarize the landslide rapid response products released by the organizations, detection methods, quantify accuracy and provide guidelines on how some of the shortcomings encountered in this effort might be addressed in the future. To support this validation, a manually mapped polygon-based landslide inventory covering the entire affected area was created and is also released through this effort.

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<p>Documenting ground deformation is important for a range of areas in Earth and environmental sci-<br>ences (such as earthquake, volcanoes, landslides and glaciers/ice sheets monitoring). In particular<br>monitoring the deformation of the cryosphere is key to understand its evolution in a context of<br>global changes, through the creation of long-term ice velocity datasets, but also possibly detect<br>failure onsets. The availability of optical satellite constellations with a frequent revisit time at medi-<br>um to high spatial resolution and an open access policy (e.g. Sentinel 2, Landsat 7/8) provides the<br>potential to contribute to ice monitoring on a global basis. However, this observational capability<br>also represents a challenge in term of storage capacity and computing resources which together<br>with the complexity of the tuning of the different parameters, may prevent users from exploiting the<br>data.</p><p><br>Here we propose a new version of the Multi-Pairwise Image Correlation for OPTical images<br>(MPIC-OPT) algorithm. The new version of the algorithm offers a complete chain to process optical<br>images including data download, image pairs creation and advanced analysis of the displacement<br>field. It offers the choice to compute the ground displacement associated to image pairs with two<br>correlation techniques (MicMac, developed by IGN; GéFolki developed by ONERA). Finally, the<br>Time-Series Inversion for Opical image (TIO) algorithm is integrated to provide displacement time<br>series.</p><p><br>The processing chain is accessible through the Geohazards Exploitation Platform (GEP) in the<br>framework of the Thematic Exploitation Platform initiative of the European Space Agency and the<br>runs are performed using the High Performance Computing facility at the A2S/Mesocentre of Uni-<br>versity of Strasbourg.</p><p><br>We present the results of the chain in various cryospheric areas: the European Alps glaciers<br>(France, Italy, Switzerland), the Astrolabe ice shelf (Antartica) and the Gangotri glacier (India). We<br>define some relevant strategies for an operational use of the service for regional monitoring of<br>land-ice from satellite images. We compare the results of the MPIC-OPT-ICE service to in-situ<br>dataset and/or results obtained with similar strategies (e.g. GoLive or ITS-LIVE products, etc.). We<br>discuss the influence of the pair network and the inversion strategy to retrieve short-term to long-<br>term kinematic regimes.</p>