Developing an Automated Method for the Application of Lidar in Iumat Land-Use Model: Analysis of Land-Use Changes Using Building-Form Parameterization, Gis, and Artificial Neural Networks

Abstract

Predicting resource consumption in the built environment and its associated environmental consequences is one of the core challenges facing policy-makers and planners seeking to increase the sustainability of urban areas. The study of land-use change has many implications for infrastructure design, resource allocation, and urban metabolism simulation. While most urban models focus on horizontal growth patterns, few investigate the impacts of vertical characteristics of urbanscapes in predicting land-use changes. In this paper, Building-form variables are introduced as a new determinant factor for investigating effects of vertical characteristics of an urbanscape in predicting land-use change. This work outlines an automated method for generating building-form variables from Light Detection and Ranging (LIDAR) data by using Density-Based Spatial Clustering and normal equations. This paper presents a Land-Use Model that uses Remote Sensing, GIS, and Artificial Neural Networks (ANNs) to predict urban growth patterns within the IUMAT framework (Integrated Urban Metabolism Analysis Tool), which is an analytical platform for quantifying the overall sustainability in the urbanscape. The town of Amherst in Western Massachusetts (for the period of 1971–2005) is used as a case study for testing the model. By isolating the weights of each explanatory variable in models, this study highlights the influence of building geometry on future development scenarios.

Scenario-Based Comprehensive Assessment for Community Resilience Adapted to Fire Following an Earthquake, Implementing the Analytic Network Process and Preference Ranking Organization Method for Enriched Evaluation II Techniques

“…To settle this issue, improving the system performance of the cities when these natural hazards occur has drawn a climbing degree of attention [8][9][10][11]. There are a lot of research scholars and participants realize that the sustainable development goals of cities should be embedded into the concept of resilience [12][13][14][15]. Hence, exploring the performance of the communities, which are the essential unit of the cities, after disasters and measuring Buildings 2021, 11, 523 2 of 24 the capability of the communities when adapting to the disasters is vitally significant and imperative [16][17][18].…”

Producing Urban Aerobiological Risk Map for Cupressaceae Family in the SW Iberian Peninsula from LiDAR Technology

“…LiDAR (light detection and ranging) has been useful for mapping possible biological risks in cities, considering slopes and geographical characteristics, the influence of buildings and other barriers within cities and their effect on pollen dispersion [24]. In addition, this technology is increasingly used in urban planning and land use [29] and for the study of the height and density of tree canopies [30]. On the other hand, Kriging is an interpolation method used as a geostatistical tool to create risk maps in different fields of research, such as in the chemical prediction of soil composition [31], in phenology [32], for the creation of aerobiological risk maps [24] or more recently to locate critical points of atmospheric pollutant concentrations in urban and peripheral areas [33].…”