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Monitoring the Yezin Dam: A Journey Through Time with Computer Vision
Introduction Nestled near Yezin village, east of the Yangon-Mandalay Highway, lies the Yezin Dam — an essential infrastructure contributing to the agricultural prosperity of Zayarthiri Township in Nay Pyi Taw City, Myanmar. Constructed in 1975, the dam’s primary purpose is to facilitate irrigation for the surrounding agricultural areas and mitigate flooding from Sinthay and Yezin Streams. Motivation for Monitoring: The Yezin Dam stands as a testament to human-engineered solutions addressing crucial water resource challenges. The motivation behind monitoring this vital structure over time is deeply rooted in understanding its dynamic interactions with the environment. By tracking changes in water levels, we aim to unravel insights into climate influences, dam management practices, and the broader implications for the region’s water resources. Google Timelapse: A Window to the Past To embark on this temporal exploration, we turn to Google Timelapse — a powerful tool harnessing the extensive satellite imagery archive from 1984 to 2022. This remarkable resource allows us to observe the evolution of landscapes, including the Yezin Dam, with unprecedented clarity. The time-lapse imagery offers a unique perspective, enabling the observation of long-term trends and environmental transformations. By leveraging Google Timelapse, we gain access to a visual chronicle of the Yezin Dam’s journey through decades. This comprehensive dataset serves as the foundation for our endeavor to employ advanced computer vision techniques, providing a nuanced understanding of how the dam and its surroundings have changed over time. Timelapse – Google Earth Engine Explore the dynamics of our changing planet over the past three and a half decades. earthengine.google.com Data Collection Insights Google Timelapse Unveiled Our exploration of the Yezin Dam’s temporal evolution commenced with the bountiful imagery from Google Timelapse, spanning 1984 to 2022. This visual journey provided a captivating narrative of the dam’s transformation over nearly four decades. Here are some sample satellite images from Google Timelapse of Yezin Dam for certain years(1996–2002): Crafting Uniformity Amidst Diversity Navigating the terrain of data collection, we confronted a formidable challenge — divergent resolutions and sizes spanning different years. Achieving analytical consistency necessitated a dedicated effort to standardize these images. The intricate yet pivotal task of harmonizing disparate shapes and sizes became our focus, demanding precision to ensure uniformity across the dataset. Amidst this endeavor, we encountered additional challenges. The unyielding nature of the images restricted any alteration to their resolution or dimensions. Attempts to upscale the images proved counterproductive, yielding unexpected and random results. Moreover, experimenting with upscaling and subsequent model application resulted in outcomes contrary to our expectations, adding another layer of complexity to the data processing journey. Despite these hurdles, we persisted with resilience, employing online tools to meticulously transform the varied images into a cohesive and standardized collection. Then we used some online tools to get the image set with equal dimensions and sizes. Model Selection: UNET Water Body Segmentation gauthamk02 / pytorch-waterbody-segmentation PyTorch implementation of image segmentation for identifying water bodies from satellite images title emoji colorFrom colorTo sdk sdk_version app_file pinned Water Body Segmentation
Introduction
Nestled near Yezin village, east of the Yangon-Mandalay Highway, lies the Yezin Dam — an essential infrastructure contributing to the agricultural prosperity of Zayarthiri Township in Nay Pyi Taw City, Myanmar. Constructed in 1975, the dam’s primary purpose is to facilitate irrigation for the surrounding agricultural areas and mitigate flooding from Sinthay and Yezin Streams.
Motivation for Monitoring: The Yezin Dam stands as a testament to human-engineered solutions addressing crucial water resource challenges. The motivation behind monitoring this vital structure over time is deeply rooted in understanding its dynamic interactions with the environment. By tracking changes in water levels, we aim to unravel insights into climate influences, dam management practices, and the broader implications for the region’s water resources.
Google Timelapse: A Window to the Past
To embark on this temporal exploration, we turn to Google Timelapse — a powerful tool harnessing the extensive satellite imagery archive from 1984 to 2022. This remarkable resource allows us to observe the evolution of landscapes, including the Yezin Dam, with unprecedented clarity. The time-lapse imagery offers a unique perspective, enabling the observation of long-term trends and environmental transformations.
By leveraging Google Timelapse, we gain access to a visual chronicle of the Yezin Dam’s journey through decades. This comprehensive dataset serves as the foundation for our endeavor to employ advanced computer vision techniques, providing a nuanced understanding of how the dam and its surroundings have changed over time.
Timelapse – Google Earth Engine
Explore the dynamics of our changing planet over the past three and a half decades.
earthengine.google.com
Data Collection Insights
Google Timelapse Unveiled
Our exploration of the Yezin Dam’s temporal evolution commenced with the bountiful imagery from Google Timelapse, spanning 1984 to 2022. This visual journey provided a captivating narrative of the dam’s transformation over nearly four decades.
Here are some sample satellite images from Google Timelapse of Yezin Dam for certain years(1996–2002):
Crafting Uniformity Amidst Diversity
Navigating the terrain of data collection, we confronted a formidable challenge — divergent resolutions and sizes spanning different years. Achieving analytical consistency necessitated a dedicated effort to standardize these images. The intricate yet pivotal task of harmonizing disparate shapes and sizes became our focus, demanding precision to ensure uniformity across the dataset.
Amidst this endeavor, we encountered additional challenges. The unyielding nature of the images restricted any alteration to their resolution or dimensions. Attempts to upscale the images proved counterproductive, yielding unexpected and random results. Moreover, experimenting with upscaling and subsequent model application resulted in outcomes contrary to our expectations, adding another layer of complexity to the data processing journey. Despite these hurdles, we persisted with resilience, employing online tools to meticulously transform the varied images into a cohesive and standardized collection.
Then we used some online tools to get the image set with equal dimensions and sizes.
