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Master/Paper of the Week84

November.2021 Week-4 A review of lane detection methods based on deep learning 2022. 5. 7.
November.2021 Week-3 Towards End-to-End Lane Detection: an Instance Segmentation Approach CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution 2022. 5. 7.
October.2021 Week-4 Mapping abundance distributions of allergenic tree species in urbanized landscapes: A nation-wide study for Belgium using forest inventory and citizen science data High Quality Monocular Depth Estimation via Transfer Learning 2022. 5. 7.
October.2021 Week-3 Effect of canopy structure on the performance of tree mapping methods in urban parks Tree mapping using airborne, terrestrial and mobile laser scanning - A case study in a heterogeneous urban forest 2022. 5. 7.
October.2021 Week-2 Mapping temperate forest tree species using dense Sentinel-2 time series Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data 2022. 5. 7.
October.2021 Week-1 A generalized multi-task learning approach to stereo DSM filtering in urban areas DANCE-NET: Density-aware convolution networks with context encoding for airborne LiDAR point cloud classification 2022. 5. 7.
September.2021 Week-4 A novel semi-supervised method for airborne LiDAR point cloud classification Identifying and mapping individual plants in a highly diverse high-elevation ecosystem using UAV imagery and deep learning 2022. 5. 7.
September.2021 Week-3 Digging Into Self-Supervised Monocular Depth Estimation Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks Real Time Object Detection, Tracking, and Distance and Motion Estimation based on Deep Learning: Application to Smart Mobility 2022. 5. 7.
September.2021 Week-2 Detection of individual trees in urban alignment from airborne data and contextual information: A marked point process approach Mapping trees along urban street networks with deep learning and street-level imagery 2022. 5. 7.
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