Abstract
Recently, settlement planning and replanning process are becoming the main problem in rapidly growing cities. Unplanned urban settlements are quite common, especially in low-income countries. Building extraction on satellite images poses another problem. The main reason for the problem is that manual building extraction is very difficult and takes a lot of time. Artificial intelligence technology, which has increased significantly today, has the potential to provide building extraction on high-resolution satellite images. This study proposes the differentiation of buildings by image segmentation on high-resolution satellite images with U-net architecture. The open-source Massachusetts building dataset was used as the dataset. The Massachusetts building dataset includes residential buildings of the city of Boston. It was aimed to remove buildings in the high-density city of Boston. In the U-net architecture, image segmentation is performed with different encoders and the results are compared. In line with the work done, 82.2% IoU accuracy was achieved in building segmentation. A high result was obtained with an F1 score of 0.9. A successful image segmentation was achieved with 90% accuracy. This study demonstrated the potential of automatic building extraction with the help of artificial intelligence in high-density residential areas. It has been determined that building mapping can be achieved with high-resolution antenna images with high accuracy achieved.
Highlights
Less-developed countries, especially in Asia and Africa, often witness new unplanned settlements like slums, urban villages, and shantytowns as the urban sprawls continue to cater to the increasing population [1]. e statistics for this problem can be known by the mapping of the settlements for nearly 564 million rural population in China [2]. ere is a rapidly increasing demand for housing facilities for the lowincome population. is demand has led to the settlement of many unplanned areas, which are highly packed with small buildings [3]
Deep learning algorithms, a subbranch of artificial intelligence, were used to provide automatic building extraction in urban settlements. us, it is foreseen that planning can be made for urban structuring by extracting buildings in urban areas on satellite images
One of the image processing methods, was preferred for building extraction. e widely used Unet model was used for image segmentation
Summary
Less-developed countries, especially in Asia and Africa, often witness new unplanned settlements like slums, urban villages, and shantytowns as the urban sprawls continue to cater to the increasing population [1]. e statistics for this problem can be known by the mapping of the settlements for nearly 564 million rural population in China [2]. ere is a rapidly increasing demand for housing facilities for the lowincome population. is demand has led to the settlement of many unplanned areas, which are highly packed with small buildings [3]. Is demand has led to the settlement of many unplanned areas, which are highly packed with small buildings [3]. Less-developed countries, especially in Asia and Africa, often witness new unplanned settlements like slums, urban villages, and shantytowns as the urban sprawls continue to cater to the increasing population [1]. While these unplanned settlements do provide housing for low-income personals, they have become to the root causes of unequal and unsatisfactory living conditions and standards. Traditional methods of cartography of unplanned construction in urban settlements require field visits, extensive measurement of constructions of interest, and manual digitalization of data. Classifying objects taken from low altitudes and via Unmanned Aerial Vehicle imaging technology is severely extensive despite some advantages over field surveys
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