Design of an Urban Domestic Waste Landfill Based on Aerial Image Segmentation and Ecological Restoration Theory

Abstract

Properly managed urban household waste landfills play a crucial role in achieving sustainable development and ecological civilization. This paper proposes an innovative design method that aims to overcome the limitations and extensive challenges of traditional urban domestic waste landfill design by combining aerial image segmentation with ecological restoration theory. We suggest an enhanced SEVnet, a combination of the basic vnet network and the sequence-and-excitation module, for precise and efficient garbage dump identification. Through this module, the network can independently analyze the significance of each feature channel and provide weights, leading to enhanced image detail recovery and more precise segmentation. We implemented the proposed SEVnet model based on an aerial photography database of urban garbage dumps. Following precise urban garbage disposal site division, we adopt ecological restoration theory to achieve sustainable design. This article presents the Saihanba area in China as a case study, collects pertinent data, and conducts image segmentation and GIS-assisted analysis. This paper examines the landscape application and design of the landfill site based on an analysis and planning of land use in its vicinity. The intervention of landscape ecology imbues the landfill site with new functions. We propose comprehensive planning and landscape restoration design measures from four perspectives, encompassing disadvantages, threats, opportunities, and strengths, using SWOT analysis to better integrate the Hebei Saihanba landfill site with the surrounding environment. This paper introduces a groundbreaking design scheme for a landfill that aligns with the principles of current green and sustainable development.

Keywords

• landfill
• deep learning
• ecological restoration
• landscape design
• SEVnet