Machine learning and SLIC for Tree Canopies segmentation in urban areas

This paper presents a novel approach combining the Simple Linear Iterative Clustering (SLIC) superpixel algorithm with a Convolutional Neural Network (CNN) over high-resolution imagery to detect trees in a typical urban environment of the Brazilian Cerrado biome. Our analysis approach for better results uses the deep learning classifier ResNet-50, with a variation in the batch size and five traditional shallow learning methods. The results were processed and avaliated using mainly accuracy as a metric, but we show that the accuracy poorly represent the overlap between the manual annotation and the resulting map, so we bring the IoU metric results to better show the Network learning classification maps results. The combined SLIC algorithm and the best CNN resulted in an accuracy of 93.20%, IoU of 0.700 and a variation of 1% for difference in the area of tree canopies if compared to our labels, while the best shallow presented an accuracy of 91.70%, IoU of 0.200 and a variation in area of 12.52%. Demonstrating that the proposed CNN method is suitable for segmenting trees from highresolution images acquired over urban environments. The segmentation with SLIC and CNN can provide very useful results for urban management using low cost RGB images. Such outcomes are of great interest for local managers since reliable maps showing the spatial distribution of trees in urban areas are often required for many applications.