ASSESSING BEACH CARRYING CAPACITY: APPLYING OBJECT DETECTION TECHNOLOGY AT SAI KAEW BEACH, KOH SAMET THAILAND
Chomchid Phromsin(a*), Kunlasatee pookpanich (a), Benjamapawn Kitjao (a), Nanticha Poonpanich (a), Punyanut Traiyatha (a)

a) Kasetsart University, Department of Geography, Faculty of Social Science, Bangkok, Thailand
*fsoccci[at]ku.ac.th

Abstract

Carrying capacity refers to the maximum number of people that can utilize a specific area. Assessing the carrying capacity of a beach involves determining the areas capacity or the maxi-mum number of visitors who can engage in activities within that beach area during a specific time period. Counting the crowd accurately poses a significant challenge in carrying capacity assessments. The aim of this paper is to apply Mask R-CNN (Region Based Convolutional Neural Networks) for detecting people and counting the number of visitors on the beach in order to evaluate the carrying capacity of Sai Kaew beach, located in Samet Island (Koh Samet), Rayong Province, Thailand between December 2019 and February 2020. This study used photo images taken by a digital camera to capture tourists on the beach by walking 4 rounds per day. The results of the research, which aimed to evaluate the accuracy of object detection using the Mask R-CNN model, indicated a precision value of 96.47% and a recall value of 92.15%. Through the application of Mask R-CNN, the study estimated the people at one time (PAOT) to be approximately 4,048 tourists per day within an 8,580 m2 area of the beach. These results contribute to the evaluation of the carrying capacity of Sai Kaew beach and provide valuable insights for managing visitor numbers and activities in the area.

Keywords: carrying capacity- beach assessment- Mask R-CNN- tourist detection

Topic: Topic A: General Remote Sensing