
Building detection with convolutional networks trained with transfer learning
Author(s) -
Simon Šanca,
Krištof Oštir,
Alen Mangafić
Publication year - 2021
Publication title -
geodetski vestnik
Language(s) - English
Resource type - Journals
eISSN - 1581-1328
pISSN - 0351-0271
DOI - 10.15292/geodetski-vestnik.2021.04.559-593
Subject(s) - orthophoto , convolutional neural network , computer science , artificial intelligence , transfer of learning , deep learning , context (archaeology) , footprint , pattern recognition (psychology) , machine learning , geography , archaeology
Building footprint detection based on orthophotos can be used to update the building cadastre. In recent years deep learning methods using convolutional neural networks have been increasingly used around the world. We present an example of automatic building classification using our datasets made of colour near-infrared orthophotos (NIR-R-G) and colour orthophotos (R-G-B). Building detection using pretrained weights from two large scale datasets Microsoft Common Objects in Context (MS COCO) and ImageNet was performed and tested. We applied the Mask Region Convolutional Neural Network (Mask R-CNN) to detect the building footprints. The purpose of our research is to identify the applicability of pre-trained neural networks on the data of another colour space to build a classification model without re-learning.