Daniel Acosta
This project measures the landslide exposure of buidling infrastructures in Portland, Oregon, using landslide susceptibility maps retrieved from SLIDO and OpenStreetMap data processed to derive building footprint maps.
Based on the quantitative landslide risk equation, which is defined as risk as a function of annual probability of landslide (hazard), the spatial and spatial-time probability of a landslide impacting a building (runout and exposure), and the probability of injury or death from a given impact by a landslide (vulnerability). In this sense, a landslide exposure is defined as the analysis of elements at risk of being prone to a hazardous zone. The elements at risk refer to building footprints that match specific susceptibility classes.
Given a landslide susceptibility map, often landslide exposure has been measured using land cover maps for further risk assessment. However, a detailed landslide exposure analysis using building footprint maps is scarce. For this reason, this project explores some ways building footprint data can be utilized to assess landslide exposure in Portland, Oregon, with a focus on building infrastructures. Further research can include social and economical features of the building zones.
What is the landslide exposure of building infrastructure in the city of Portland, Oregon? The dataset to be used are:
The simple method to quantify landslide exposure is by overlapping the susceptibility map with the building footprint and measure how much raster area corresponds to each susceptibility class.
Tools packages (libraries):
import os
import requests
import numpy as np
import pandas as pd
import geopandas as gpd
import rasterio as rio
from rasterio import plot, mask
from rasterio.warp import calculate_default_transform, reproject, Resampling
import matplotlib.pyplot as plt
import rasterstats
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