Affiliated Fusion Conditional Random Field for Urban UAV Image Semantic Segmentation Unmanned aerial vehicles (UAV) have had significant progress in the last decade, which is applied to many relevant fields because of the progress of aerial image processing and the convenience to explore areas that men cannot reach. Still, as the basis of further applications such as object tracking and terrain classification, semantic image segmentation is one of the most difficult challenges in the field of computer vision. In this paper, we propose a method for urban UAV images semantic segmentation, which utilizes the geographical information of the region of interest in the form of a digital surface model (DSM). We introduce an Affiliated Fusion Conditional Random Field (AFCRF), which combines the information of visual pictures and DSM, and a multiscale strategy with attention to improve the segmenting results. The experiments show that the proposed structure performs better than stateoftheart networks in multiple metrics.

Informed Region Selection for Efficient UAVbased Object Detectors: Altitudeaware Vehicle Detection with CyCAR Dataset Deep Learningbased object detectors enhance the capabilities of remote sensing platforms, such as Unmanned Aerial Vehicles (UAVs), in a wide spectrum of machine vision applications. However, the integration of deep learning introduces heavy computational requirements, preventing the deployment of such algorithms in scenarios that impose lowlatency constraints during inference, in order to make missioncritical decisions in realtime. In this paper, we address the challenge of efficient deployment of regionbased object detectors in aerial imagery, by introducing an informed methodology for extracting candidate detection regions (proposals). Our approach considers information from the UAV onboard sensors, such as flying altitude and lightweight computer vision filters, along with prior domain knowledge to intelligently decrease the number of region proposals by eliminating falsepositives at an early stage of the computation, reducing significantly the computational workload while sustaining the detection accuracy. We apply and evaluate the proposed approach on the task of vehicle detection. Our experiments demonstrate that stateoftheart detection models can achieve up to 2.6x faster inference by employing our altitudeaware datadriven methodology. Alongside, we introduce and provide to the community a novel vehicleannotated and altitudestamped dataset of real UAV imagery, captured at numerous flying heights under a wide span of traffic scenarios.