{"id":1239,"date":"2026-10-28T11:28:40","date_gmt":"2026-10-28T11:28:40","guid":{"rendered":"https:\/\/symposium.earsel.org\/46th-symposium-Turin\/?page_id=1239"},"modified":"2026-10-28T13:48:39","modified_gmt":"2026-10-28T13:48:39","slug":"earth-observation-for-the-social-sciences-methods-applications-and-critical-perspectives-at-the-pixel-people-interface","status":"publish","type":"page","link":"https:\/\/symposium.earsel.org\/46th-symposium-Turin\/?page_id=1239","title":{"rendered":"Earth Observation for the Social Sciences. Methods, applications and critical perspectives at the pixel\u2013people interface"},"content":{"rendered":"\n<h4 class=\"wp-block-heading\">Scope and topics<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Earth Observation (EO) is now indispensable to the environmental and physical sciences, yet its uptake in the social<br>sciences remains comparatively limited. Social conditions such as poverty, deprivation, segregation, wellbeing, mobility,<br>access to services have no direct spectral signature; they must be inferred through proxies, models, contextual<br>knowledge and careful validation. Doing this credibly demands methodological work that sits between remote sensing<br>and social science: representation learning that captures socially meaningful structure, principled fusion of EO with<br>surveys, censuses, registers and participatory data, explicit treatment of uncertainty and bias, and research designs that<br>support inference rather than description alone. It also raises conceptual and ethical questions about what is being<br>measured, for whom, and with what consequences for the people represented in the data.<br>This session brings together researchers developing and critically examining EO-based approaches to social scientific<br>questions across both data-scarce and data-rich settings. Contributions from the Global South, where EO frequently<br>substitutes for missing statistical infrastructure, are as welcome as work from register-rich contexts such as the Nordic<br>countries, where EO instead complements and spatially disaggregates existing micro-data. Comparative work, and<br>studies testing the transferability of approaches between these settings, are explicitly encouraged, as are validation<br>studies, benchmark comparisons and negative results, which remain under-reported in this literature.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>List of topics:<\/strong><\/p>\n\n\n\n<div class=\"wp-block-group is-vertical is-layout-flex wp-container-core-group-is-layout-4fc3f8e1 wp-block-group-is-layout-flex\">\n<ul class=\"wp-block-list\">\n<li>EO-based measurement of poverty, deprivation, inequality, segregation and living conditions<\/li>\n\n\n\n<li>Population distribution and dynamics, mobility, migration and forced displacement<\/li>\n\n\n\n<li>Fusion of EO with censuses, surveys, administrative registers, citizen science and participatory data<\/li>\n\n\n\n<li>GeoAI for socio-economic inference: representation learning, geospatial foundation models, vision\u2013language<br>models<\/li>\n\n\n\n<li>Explainability, uncertainty quantification, bias, fairness and validation of EO-derived social indicators<\/li>\n\n\n\n<li>Causal inference and policy evaluation using EO-derived measures<\/li>\n\n\n\n<li>Longitudinal and historical social analysis using archival imagery and historical cartographic sources<\/li>\n\n\n\n<li>EO for public health, service accessibility, environmental justice and exposure to climate risk<\/li>\n\n\n\n<li>Armed conflict, food security and crisis analytics<\/li>\n\n\n\n<li>Ethics, privacy, governance, and inequalities in access to EO data, infrastructure and expertise<\/li>\n\n\n\n<li>Open, reproducible and FAIR workflows; capacity building and training for social scientists<\/li>\n<\/ul>\n<\/div>\n\n\n\n<h4 class=\"wp-block-heading\">Organizers<\/h4>\n\n\n\n<div class=\"wp-block-group is-vertical is-layout-flex wp-container-core-group-is-layout-4fc3f8e1 wp-block-group-is-layout-flex\">\n<p class=\"wp-block-paragraph\"><strong>Ola Hall<\/strong> Dept. of Human Geography, Lund University, Sweden<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Stefanos Georganos<\/strong>, Geomatics, Dept. of Environmental and Life Sciences, Karlstad University, Sweden<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><br><strong>Lina Eklund<\/strong> Dept. of Earth and Environmental Sciences, Lund University, Sweden<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Adel Daoud<\/strong> ,Institute for Analytical Sociology, Link\u00f6ping University &amp; Dept. of Computer Science and Engineering, Chalmers University of Technology, Sweden<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><br><strong>Lia Antoniou<\/strong> , LF Research Foundation, Sweden<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><br><strong>Sai Ganesh Veeravalli<\/strong>, Risk and Environmental Studies, KarlstadUniversity, Sweden<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Scope and topics Earth Observation (EO) is now indispensable to the environmental and physical sciences, yet its uptake in the socialsciences remains comparatively limited. Social conditions such as poverty, deprivation, segregation, wellbeing, mobility,access to services have no direct spectral signature; they must be inferred through proxies, models, contextualknowledge and careful validation. Doing this credibly demands [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-1239","page","type-page","status-publish","hentry"],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"SymAth27","author_link":"https:\/\/symposium.earsel.org\/46th-symposium-Turin\/?author=3"},"uagb_comment_info":0,"uagb_excerpt":"Scope and topics Earth Observation (EO) is now indispensable to the environmental and physical sciences, yet its uptake in the socialsciences remains comparatively limited. Social conditions such as poverty, deprivation, segregation, wellbeing, mobility,access to services have no direct spectral signature; they must be inferred through proxies, models, contextualknowledge and careful validation. Doing this credibly demands&hellip;","_links":{"self":[{"href":"https:\/\/symposium.earsel.org\/46th-symposium-Turin\/index.php?rest_route=\/wp\/v2\/pages\/1239","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/symposium.earsel.org\/46th-symposium-Turin\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/symposium.earsel.org\/46th-symposium-Turin\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/symposium.earsel.org\/46th-symposium-Turin\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/symposium.earsel.org\/46th-symposium-Turin\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1239"}],"version-history":[{"count":3,"href":"https:\/\/symposium.earsel.org\/46th-symposium-Turin\/index.php?rest_route=\/wp\/v2\/pages\/1239\/revisions"}],"predecessor-version":[{"id":1289,"href":"https:\/\/symposium.earsel.org\/46th-symposium-Turin\/index.php?rest_route=\/wp\/v2\/pages\/1239\/revisions\/1289"}],"wp:attachment":[{"href":"https:\/\/symposium.earsel.org\/46th-symposium-Turin\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1239"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}