EarthQA: A Question Answering Engine for Earth Observation Data Archives

<p>The standard way for earth observation experts or users to retrieve images from image archives (e.g., ESA's Copernicus Open Access Hub) is to use a graphical user interface, where they can select the geographical area of the image they are interested in and additionally they can specify some other metadata, such as sensing period, satellite platform and cloud cover.</p> <p>In this work, we are developing the question-answering engine EarthQA that takes as input a question expressed in <em>natural language</em> (English) that asks for satellite images satisfying certain criteria and returns links to such datasets, which can be then downloaded from the CREODIAS cloud platform. To answer user questions, EarthQA queries two interlinked <em>knowledge graphs</em>: a knowledge graph encoding metadata of satellite images from the CREODIAS cloud platform (the SPARQL endpoint of CREODIAS) and the well-known knowledge graph DBpedia. Hence, the questions can refer to image metadata (e.g., satellite platform, sensing period, cloud cover), but also to more generic entities appearing in DBpedia knowledge graph (e.g., lake, Greece). In this way, the users can ask questions like &#8220;Find all Sentinel-1 GRD images taken during October 2021 that show large lakes in Greece having an area greater than 100 square kilometers&#8221;.</p> <p>EarthQA follows a template-based approach to translate natural language questions into formal queries (SPARQL). Initially, it decomposes the user question by generating its dependency parse tree and then automatically disambiguates the components appearing in the question to elements of the two knowledge graphs.&#160; In particular, it automatically identifies the spatial or temporal entities (e.g., &#8220;Greece&#8221;, &#8220;October 2021&#8221;), concepts (e.g., &#8220;lake&#8221;), spatial or temporal relations (e.g., &#8220;in&#8221;, &#8220;during&#8221;), properties (e.g., &#8220;area&#8221;) and product types (e.g., &#8220;Sentinel-1 GRD&#8221;) and other metadata (e.g., &#8220;cloud cover below 10%&#8221;) mentioned in the question and maps them to the respective elements appearing in the two knowledge graphs (dbr:Greece, dbo:Lake, dbp:area, etc). After this, the SPARQL query is automatically generated.</p>