Abstract
Food resources face severe damages under extraordinary situations of catastrophes such as earthquakes, cyclones, and tsunamis. Under such scenarios, speedy assessment of food resources from agricultural land is critical as it supports aid activity in the disaster hit areas. In this article, a deep learning approach is presented for the detection and segmentation of coconut tress in aerial imagery provided through the AI competition organized by the World Bank in collaboration with OpenAerialMap and WeRobotics. Maked Region-based Convolutional Neural Network approach was used identification and segmentation of coconut trees. For the segmentation task, Mask R-CNN model with ResNet50 and ResNet1010 based architectures was used. Several experiments with different configuration parameters were performed and the best configuration for the detection of coconut trees with more than 90% confidence factor was reported. For the purpose of evaluation, Microsoft COCO dataset evaluation metric namely mean average precision (mAP) was used. An overall 91% mean average precision for coconut trees detection was achieved.
Highlights
Natural disasters in the Kingdom of Tonga (South Pacific) are an unfortunate global reality
The World Bank seeks qualified teams to develop machine‐ learning‐based methods to automate the assessment of aerial imagery and to classify and locate the standing trees such as coconut trees within the aerial snapshot [3]
The weights of this model is applied and trained for 21 epochs to detect coconut trees on the test‐set that consists of 10 images6
Summary
Natural disasters in the Kingdom of Tonga (South Pacific) are an unfortunate global reality. Their consequences can be damaging for the south Pacific population who heavily depend on the local agriculture as a primary food source [1]. There is a great demand to reinforce food security mechanisms and make appropriate assessments of the damages caused [2]. When cyclones strike, recognising the area of damage is crucial for effective humanitarian response and securing undamaged food sources like the coconut trees. Manual aerial image classification is a resource and skill−intensive task and requires a lot of time. Manual aerial image classification is not typically risk‐free in disaster‐hit regions
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