Self-supervised approach for Urban Tree Recognition on Aerial Images

Babu Saheer, Lakshmi and Shahawy, Mohamed (2021) Self-supervised approach for Urban Tree Recognition on Aerial Images. In: AIAI 2021: Artificial Intelligence Applications and Innovations, Online.

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Official URL: https://doi.org/10.1007/978-3-030-79157-5_39

Abstract

In the light of Artificial Intelligence aiding modern society in tackling climate change, this research looks at how to detect vegetation from aerial view images using deep learning models. This task is part of a proposed larger framework to build an eco-system to monitor air quality and the related factors like weather, transport, and vegetation, as the number of trees for any urban city in the world. The challenge involves building or adapting the tree recognition models to a new city with minimum or no labeled data. This paper explores self-supervised approaches to this problem and comes up with a system with 0.89 mean average precision on the Google Earth images for Cambridge city.

Item Type: Conference or Workshop Item (Paper)
Keywords: Self supervised model, Urban Tree Recognition
Faculty: Faculty of Science & Engineering
SWORD Depositor: Symplectic User
Depositing User: Symplectic User
Date Deposited: 11 Jun 2021 10:08
Last Modified: 14 Apr 2022 14:59
URI: https://arro.anglia.ac.uk/id/eprint/706657

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