Cloud Computation Using High-Resolution Images for Improving the SDG Indicator on Open Spaces

Abstract

Open spaces are essential for promoting quality of life in cities. However, accelerated urban growth, in particular in cities of the global South, is reducing the often already limited amount of open spaces with access to citizens. The importance of open spaces is promoted by SDG indicator 11.7.1; however, data on this indicator are not readily available, neither globally nor at the metropolitan scale in support of local planning, health and environmental policies. Existing global datasets on built-up areas omit many open spaces due to the coarse spatial resolution of input imagery. Our study presents a novel cloud computation-based method to map open spaces by accessing the multi-temporal high-resolution imagery repository of Planet. We illustrate the benefits of our proposed method for mapping the dynamics and spatial patterns of open spaces for the city of Kampala, Uganda, achieving a classification accuracy of up to 88% for classes used by the Global Human Settlement Layer (GHSL). Results show that open spaces in the Kampala metropolitan area are continuously decreasing, resulting in a loss of open space per capita of approximately 125 m2 within eight years.

Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda

“…Broad-scale (i.e., global and continental) satellite-driven human settlement products, including the Global Human Settlement Layer (GHSL; [1,2]), the World Settlement Footprint (WSF; [3]), the High Resolution Settlement Layer (HRSL; [4]) and Geo-Referenced Infrastructure and Demographic Data for Development settlement extent data (GRID3-SE; [5]) seek to represent the presence and extent of populated built-up regions across an array of urban and rural geographies [6]. These products have broadened our awareness of where humans live and work [7][8][9] and have made important contributions to population modeling [10][11][12][13][14], development monitoring [8,[15][16][17] and climate hazard mitigation [18][19][20][21].…”

Special Issue “Remote-Sensing-Based Urban Planning Indicators”

“…EO data offer manifold opportunities for mapping and monitoring urban areas [18][19][20][21][22]. They serve to derive various physical, climatic, and socio-economic indicators in support of urban planning, emergency response, and decision making [23].…”

“…This allows for analysing and comparing conditions among different urban settlements, cities, and countries, and for different years. For this reason, EO is also a fundamental data source for tracking the progress towards the SDGs and monitoring target indicators, as well as providing actionable information for local, regional, and state governments [19,[24][25][26]. Once translated into regularly updated geospatial information and knowledge, these data can support strategic planning and interventions responding to the multiple challenges related to rapid population growth, scarcity of resources, and increasing frequency and intensity of natural hazards caused by a changing climate.…”

Seamless Mosaic Using Removal Sunglint to Support a Large-Scale Mapping Acceleration

“…A remote sensing image mosaic is one of the steps in the data preparation process to be disseminated to users, wherein general, users need a seamless, mosaic image, especially on the land area (Aguilar & Kuffer, 2020;Cassol et al, 2020;Fassnacht et al, 2019;Sarzynski et al, 2020). In this study, the system used to perform the mosaic process uses Pixel Factory (PF), where PF is a unique software for processing remote sensing data from both satellite and aerial photographs.…”