Monitoring tree cover in an area plays an important role in a wide range of applications and advances in UAV technology has made it feasible to capture high resolution imagery which can be used for this purpose. In this study, we adopt a state of the art object detector Mask Region-based CNN (Mask R-CNN1), through transfer learning, for the task of tree segmentation and counting. One bottleneck for the proposed task is the huge amount of data required if the model is required to be scalable to various different geographical regions. Towards this end, we explore the use of a sampling technique based on Gist descriptors and Gabor filtering in order to minimize the amount of training data required for obtaining excellent model performance across images with varied geographical features. This study was conducted across four regions in India, each having a different geographical landscape. We captured a total of 2357 images across all four regions. The final training dataset comprised of 48 images (sampled using the aforementioned method), representative of the entire dataset. Our method demonstrates high quality and scalable tree detection results.
Building and expansion of an efficient transportation network are essential for urban city advancement. However, tracking road development in an area is not an easy task as city planners do not always have access to credible information. A road network mapping framework is proposed which uses a random forest model for pixel-wise road segmentation. Road detection is followed by computer vision post-processing steps including Connected Component Analysis (CCA) and Hough Lines method for network extraction from high-resolution aerial images. The custom dataset used consists of images collected from an urban settlement in India.
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