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About Aerial Imagery Pixel-Level Segmentation
This document discusses using neural networks to perform pixel-level semantic segmentation on aerial imagery. It aims to close the performance gap between popular datasets and aerial imagery analysis by achieving over 70% mean intersection-over-union on the DroneDeploy validation set using DeepLabv3+ Xception65 architecture. This establishes a new state-of-the-art benchmark for the DroneDeploy test set of 52.5% mean intersection-over-union. The document provides background on aerial imagery analysis, related work on aerial image segmentation techniques, and experiments to optimize a model for this task.
Detailed Information
| Author: | ['Suanny Fabyne'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 30 |
| Language: | English |
| Format: | |
| Price: | FREE |
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