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Found 11 papers, 0 papers with code
Drones4Good: Supporting Disaster Relief Through Remote Sensing and AI
In order to respond effectively in the aftermath of a disaster, emergency services and relief organizations rely on timely and accurate information about the affected areas.
Multiple Pedestrians and Vehicles Tracking in Aerial Imagery: A Comprehensive Study
We also describe our proposed Deep Learning based Multi-Object Tracking method AerialMPTNet that fuses appearance, temporal, and graphical information using a Siamese Neural Network, a Long Short-Term Memory, and a Graph Convolutional Neural Network module for a more accurate and stable tracking.
EAGLE: Large-scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery
To address this issue, we introduce EAGLE (oriEnted vehicle detection using Aerial imaGery in real-worLd scEnarios), a large-scale dataset for multi-class vehicle detection with object orientation information in aerial imagery.
SkyScapes -- Fine-Grained Semantic Understanding of Aerial Scenes
We have defined two main tasks on this dataset: dense semantic segmentation and multi-class lane-marking prediction.
AerialMPTNet: Multi-Pedestrian Tracking in Aerial Imagery Using Temporal and Graphical Features
Due to the challenges such as the large number and the tiny size of the pedestrians (e. g., 4 x 4 pixels) with their similar appearances as well as different scales and atmospheric conditions of the images with their extremely low frame rates (e. g., 2 fps), current state-of-the-art algorithms including the deep learning-based ones are unable to perform well.
SkyScapes Fine-Grained Semantic Understanding of Aerial Scenes
We have defined two main tasks on this dataset: dense semantic segmentation and multi-class lane-marking prediction.
Ranked #1 on
Semantic Segmentation
on SkyScapes-Lane
ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery
On-board real-time vehicle detection is of great significance for UAVs and other embedded mobile platforms.
Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery
During training, we minimize joint horizontal and oriented bounding box loss functions, as well as a novel loss that enforces oriented boxes to be rectangular.
Ranked #49 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks
Therefore, accurate and reliable lane marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals.
Road Segmentation in SAR Satellite Images with Deep Fully-Convolutional Neural Networks
Remote sensing is extensively used in cartography.
Advanced Steel Microstructural Classification by Deep Learning Methods
In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel.
