Implementation of Image Processing and Machine Learning in High Resolution Aerial Image Datasets for Lake Resource Usage, Aquaculture, and Coastal Community

• Main Authors: Belarmino, Mark Daniel, Libatique, Nathaniel Joseph C
• Format: text
• Published: Archīum Ateneo 2020
• Subjects: Lakes; Fish; Aquaculture; Image segmentation; Urban areas; Belts; Training; Lake Aquaculture; Coastal Settlements; Image Processing; Machine Learning; Aquaculture and Fisheries; Electrical and Computer Engineering
• Online Access: https://archium.ateneo.edu/ecce-faculty-pubs/114; https://ieeexplore.ieee.org/document/9293932
• Institution: Ateneo De Manila University

Last May 2019, fish farms in Taal Lake suffer from fish kill resulting in an estimated loss of 405 tons of fish. It was reported that the measured water sample from the lake shows significant loss of dissolved-oxygen due to over-crowding of fish farm. With the crisis mentioned, recent studies utilize satellite remote sensors to map and monitor the aquaculture inside the lake. The maps are being used as reference material for progress monitoring, as decision-support and lake management tool by the local government and regulatory agencies. Aerial maps were captured using Unmanned Aerial Vehicle (UAV) as it has better resolution than satellite imagery. This study implements image processing and Mask Regional Convolutional Neural Network (Mask RCNN) on high resolution images to create an object detection and segmentation model for aquaculture structures and coastal settlement. To create the detection model, the image dataset undergoes preprocessing before feeding into the training process. Finally, an analytical software was developed to utilize segmented maps for zone management plan implementation, lake resource usage calculation, and gauge the population of settlers along the coastline. This provides meaningful visual and statistical data regarding aquaculture population, lake resource usage, local settlement population and zone development plan status.