000161081 001__ 161081
000161081 005__ 20251017144623.0
000161081 0247_ $$2doi$$a10.1007/s12145-025-01926-6
000161081 0248_ $$2sideral$$a144245
000161081 037__ $$aART-2025-144245
000161081 041__ $$aeng
000161081 100__ $$0(orcid)0000-0002-2112-3478$$aSuaza-Medina, Mario E.$$uUniversidad de Zaragoza
000161081 245__ $$aDetection of changes in the heat emissions signature of buildings related to indoor activity using publicly available satellite data
000161081 260__ $$c2025
000161081 5060_ $$aAccess copy available to the general public$$fUnrestricted
000161081 5203_ $$aMonitoring human activities in remote areas presents significant challenges due to lacking communication networks and infrastructure. In this context, using publicly available satellite imagery offers a cost-effective solution, as it enables the identification of changes in these areas. However, specific scenarios make detection more complicated. One such scenario is detecting indoor activity within buildings in remote areas. Walls and roofs create barriers for most sensors. Nevertheless, activities inside buildings can be associated with heat emissions, which specific remote sensors can detect. Unfortunately, publicly available satellite data does not include information from such sensors. In light of this limitation, this study investigates the opportunity of using machine learning models to interpret public-available data. Specifically, we trained four machine learning models (XGBoost, LGBM, DNN, and CNN) using images from Sentinel-2 Band 12 (the sensor with the frequency range closest to the heat emission peak) and meteorological data (temperature). Our results show that these models can identify farm-building activity, with the XGBoost model achieving the highest accuracy of 0.96 by integrating satellite data and temperature information; the findings suggest that leveraging public satellite sensors can effectively detect human heat emissions and improve surveillance in remote areas, overcoming some limitations of traditional methods.
000161081 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2020-113353RB-I00$$9info:eu-repo/grantAgreement/ES/DGA/T59-23R
000161081 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000161081 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000161081 700__ $$0(orcid)0000-0003-3071-5819$$aLacasta, Javier$$uUniversidad de Zaragoza
000161081 700__ $$0(orcid)0000-0001-6491-7430$$aLópez-Pellicer, Francisco J.$$uUniversidad de Zaragoza
000161081 700__ $$0(orcid)0000-0001-7866-3793$$aBéjar, Rubén$$uUniversidad de Zaragoza
000161081 700__ $$0(orcid)0000-0002-6557-2494$$aZarazaga-Soria, F. Javier$$uUniversidad de Zaragoza
000161081 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000161081 773__ $$g18, 2 (2025), 420 [17 pp.]$$pEARTH SCIENCE INFORMATICS$$tEARTH SCIENCE INFORMATICS$$x1865-0473
000161081 8564_ $$s1136674$$uhttps://zaguan.unizar.es/record/161081/files/texto_completo.pdf$$yVersión publicada
000161081 8564_ $$s2334360$$uhttps://zaguan.unizar.es/record/161081/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000161081 909CO $$ooai:zaguan.unizar.es:161081$$particulos$$pdriver
000161081 951__ $$a2025-10-17-14:22:49
000161081 980__ $$aARTICLE