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#6297. Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement
| September 2026 | publication date |
| Proposal available till | 20-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Computers in Earth Sciences; Computer Science Applications; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
The detection of informal settlements is the first step in planning and upgrading deprived areas in order to leave no one behind in SDGs. Very High-Resolution satellite images (VHR), have been extensively used for this purpose. However, as a cost-prohibitive data source, VHR might not be available to all, particularly nations that are home to many informal settlements. This study examines the application of open and freely available data sources to detect the structure and pattern of informal settlements. Here, in a case study of Jakarta, Indonesia, Medium Resolution satellite imagery (MR) derived from Landsat 8 (20XX) was classified to detect these settlements. The classification was done using Random Forest (RF) classifier through two complementary approaches to develop the training set. In the first approach, available survey data sets (Jakarta’s informal settlements map for 20XX) and visual interpretation using High-Resolution Google Map imagery have been used to build the training set. Throughout the second round of classification, OpenStreetMap (OSM) layers were used as the complementary approach for training.
Keywords:
informal settlement detection; machine learning; medium resolution satellite imagery (MR); open data; OpenStreetMap (OSM); random forest (RF); sustainable development goals (SDGs)
Contacts :
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
The detection of informal settlements is the first step in planning and upgrading deprived areas in order to leave no one behind in SDGs. Very High-Resolution satellite images (VHR), have been extensively used for this purpose. However, as a cost-prohibitive data source, VHR might not be available to all, particularly nations that are home to many informal settlements. This study examines the application of open and freely available data sources to detect the structure and pattern of informal settlements. Here, in a case study of Jakarta, Indonesia, Medium Resolution satellite imagery (MR) derived from Landsat 8 (20XX) was classified to detect these settlements. The classification was done using Random Forest (RF) classifier through two complementary approaches to develop the training set. In the first approach, available survey data sets (Jakarta’s informal settlements map for 20XX) and visual interpretation using High-Resolution Google Map imagery have been used to build the training set. Throughout the second round of classification, OpenStreetMap (OSM) layers were used as the complementary approach for training.
Keywords:
informal settlement detection; machine learning; medium resolution satellite imagery (MR); open data; OpenStreetMap (OSM); random forest (RF); sustainable development goals (SDGs)
Contacts :
