ALADIM &#8211; A change detection on-line service for landslide detection from EO imagery.
Authors: Deprez Aline , Marc Odin , Malet Jean-Philippe , Stumpf André , Michéa David 
Published: March 2022

&lt;p&gt;Mapping landslides after major triggering events (earthquake, large rainfall) is crucial for disaster response, hazard assessment, as well as for having benchmark inventories on which landslide models can be tested. Numerous studies have already demonstrated the utility of very-high resolution satellite and aerial images for the elaboration of inventories based on semi-automatic methods or visual image interpretation. However, while manual methods are very time consuming, faster semi-automatic methods are rarely used in an operational contexts, partly caused by data access restrictions on the required input (i.e. VHR satellite images) and by the absence of dedicated services (i.e. processing chain) available for the landslide community.&lt;/p&gt;&lt;p&gt;From a data perspective, the free access to the Sentinel-2 and Landsat-8 missions offers opportunities for the design of an operational service that can be deployed for landslide inventory mapping at any time and everywhere on the Earth. From a processing perspective, the Geohazards Exploitation Platform &amp;#8211;GEP&amp;#8211; of the European Space Agency &amp;#8211;ESA&amp;#8211; allows the access to processing algorithms in a high computing performance environment. And, from a community perspective, the Committee on Earth Observation Satellites (CEOS) has targeted the take-off of such service as a main objective for the landslide and risk community.&lt;/p&gt;&lt;p&gt;Within this context, we present a largely automatic, supervised image processing chain for landslide inventory mapping. The workflow includes:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;A &lt;strong&gt;segmentation &lt;/strong&gt;step, which performances is optimized in terms of precision and computing time and with respect to the input data resolution.&lt;/li&gt;
&lt;li&gt;A &lt;strong&gt;feature extraction&lt;/strong&gt; step, consisting in the computation of a large set of features (spectral, textural, topographic, morphometric) for the candidate segments to be classified;&lt;/li&gt;
&lt;li&gt;A &lt;strong&gt;per object classification &lt;/strong&gt;, based on the training of a random-forest classifier from a sample of manually mapped landslide polygons .&lt;/li&gt;
&lt;/ul&gt;&lt;p&gt;The service is able to process both HR (Sentinel-2 or Landsat-8) and VHR (Pl&amp;#233;iades, SPOT, Planet, Geo-eyes or every multi-spectral image with 4 bands, blue, green, red, NIR) sensors. The service can be operated in two modes (bi-dates, single-date; the bi-dates mode is based on change detection methods with images before and after a given event, whereas the mono-date mode allows a mapping of landcover at any given time). &amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;The service is presented on use cases with both medium resolution (Sentinel-2, Landsat-8) and high-resolution (Spot-6,7, Pl&amp;#233;iades) images corresponding landscapes recently impacted by landslide disasters (e.g. Haiti, Mozambique, Kenya). The landslide inventory maps are provided with uncertainty maps that allows identifying areas which might require further considerations.&lt;/p&gt;&lt;p&gt;Although the initial focus and the main usage of ALADIM is associated with the landslide analyses, there is a large panel of possible applications. The processing chain was already tested in different others contexts (urbanization, deforestation, agricultural land change, &amp;#8230;) with very promising results.&lt;/p&gt;