{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T11:00:28Z","timestamp":1774090828559,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,7,3]],"date-time":"2021-07-03T00:00:00Z","timestamp":1625270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federal Ministry for Economic Affairs and Energy","award":["none"],"award-info":[{"award-number":["none"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This contribution focuses on the utilization of very-high-resolution (VHR) images to identify construction areas and their temporal changes aiming to estimate the investment in construction as a basis for economic forecasts. Triggered by the need to improve macroeconomic forecasts and reduce their time intervals, the idea arose to use frequently available information derived from satellite imagery. For the improvement of macroeconomic forecasts, the period to detect changes between two points in time needs to be rather short because early identification of such investments is beneficial. Therefore, in this study, it is of interest to identify and quantify new construction areas, which will turn into build-up areas later. A multiresolution segmentation followed by a kNN classification is applied to WorldView images from an area around the southern part of Berlin, Germany. Specific material compositions of construction areas result in typical classification patterns different from other land cover classes. A GIS-based analysis follows to extract specific temporal \u201cpatterns of life\u201d in construction areas. With the early identification of such patterns of life, it is possible to predict construction areas that will turn into real estate later. This information serves as an input for macroeconomic forecasts to support quicker forecasts in future.<\/jats:p>","DOI":"10.3390\/rs13132618","type":"journal-article","created":{"date-parts":[[2021,7,4]],"date-time":"2021-07-04T22:35:22Z","timestamp":1625438122000},"page":"2618","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2338-7987","authenticated-orcid":false,"given":"Carsten","family":"Juergens","sequence":"first","affiliation":[{"name":"Geomatics Group, Geography Department, Ruhr University Bochum, D-44801 Bochum, Germany"}]},{"given":"M. Fabian","family":"Meyer-He\u00df","sequence":"additional","affiliation":[{"name":"Geomatics Group, Geography Department, Ruhr University Bochum, D-44801 Bochum, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,3]]},"reference":[{"key":"ref_1","unstructured":"RWI\u2014Leibniz-Institut f\u00fcr Wirtschaftsforschung (2021). Big Data in der Makro\u00f6konomischen Analyse. Fachlos 3: Machbarkeitsstudie: Prognose von Ausr\u00fcstungsinvestitionen, Bauinvestitionen, Exporten mit Unkonventionellen Datenquellen und Methoden, Vorl\u00e4ufiger Endbericht."},{"key":"ref_2","unstructured":"Rashed, T., and J\u00fcrgens, C. (2005). Remote Sensing of Urban and Suburban Areas, Springer. [1st ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Weng, Q., and Quattrochi, D. (2006). Urban Remote Sensing, CRC Press.","DOI":"10.1201\/b15917"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Henits, L., J\u00fcrgens, C., and Mucsi, L. (2016). 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