| Title |
Building height prediction using neural networks based on Sentinel multi spectral images / |
| Authors |
Stravinskas, Giedrius ; Vadapolas, Vytas ; Pamakštis, Arminas ; Kriščiūnas, Andrius ; Lagzdinytė-Budnikė, Ingrida |
| Full Text |
|
| Is Part of |
CEUR workshop proceedings: proceedings of the 27th international conference on information society and university studies (IVUS 2022), Kaunas, Lithuania, May 12, 2022 / edited by: T. Krilavičius, A. Lopata, I. Veitaitė, M. Woźniak, Ch. Napoli, D. Kalinauskaitė.. Aachen : CEUR-WS. 2022, vol. 3611, p. 70-76.. ISSN 1613-0073 |
| Keywords [eng] |
building height prediction ; Sentinel images ; artificial neural networks |
| Abstract [eng] |
Satellite imagery is a form of data that can be used for many applications, especially those focusing on change over time. In this article, we analyze methods of detecting buildings and predicting their height as well as what key attributes are required for good predictions. Building detection and prediction are done by using neural network algorithms such as convolutional neural networks to estimate their height. Predictions are made based on additional data of the building and its area. In this paper three different building estimation models are implemented. The research showed that using a mixed dataset that takes both Sentinel image patch data and numerical feature input of additional building data performs well even with lower quality images. |
| Published |
Aachen : CEUR-WS |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2022 |
| CC license |
|
