Ünsalan, CemTurgay, Zeynep ZerrinKüçükaydın, HandeHöke, Berkan2021-06-192021-06-192021Höke, B., Turgay, Z., Ünsalan, C., & Küçükaydın, H. (31/05/2021). Determining and evaluating new store locations using remote sensing and machine learning. Turkish Journal of Electrical Engineering & Computer Sciences, 29(3). p.1509-1523.1300-0632https://hdl.handle.net/20.500.11779/1499https://doi.org/10.3906/elk-2005-202Decision making for store locations is crucial for retail companies as the profit depends on the location. The key point for correct store location is profit approximation, which is highly dependent on population of the corresponding region, and hence, the volume of the residential area. Thus, estimating building volumes provides insight about the revenue if a new store is about to be opened there. Remote sensing through stereo/tri-stereo satellite images provides wide area coverage as well as adequate resolution for three dimensional reconstruction for volume estimation. We reconstruct 3D map of corresponding region with the help of semiglobal matching and mask R-CNN algorithms for this purpose. Using the existing store data, we construct models for estimating the revenue based on surrounding building volumes. In order to choose the right location, the suitable utility model, which calculates store revenues, shouldbe rigorously determined. Moreover, model parameters should be assessed as correctly as possible. Instead of using randomly generated parameters, we employ remote sensing, computer vision, and machine learning techniques, which provide a novel way for evaluating new store locations.eninfo:eu-repo/semantics/openAccessRevenue estimationCompetitive facility locationMachine learningUtility modelRemote sensingArticle10.3906/elk-2005-2022-s2.0-85108346790