Mapping human settlements with higher accuracy and less volunteer efforts by combining crowdsourcing and deep learning
Reliable techniques to generate accurate data sets of human built-up areas at national,
regional, and global scales are a key factor to monitor the implementation progress of the …
regional, and global scales are a key factor to monitor the implementation progress of the …
Deep learning from multiple crowds: A case study of humanitarian mapping
Satellite images are widely applied in humanitarian mapping that labels buildings, roads,
and so on for humanitarian aid and economic development. However, the labeling now is …
and so on for humanitarian aid and economic development. However, the labeling now is …
[HTML][HTML] A census from heaven: Unraveling the potential of deep learning and Earth Observation for intra-urban population mapping in data scarce environments
Urban population distribution maps are vital elements for monitoring the Sustainable
Development Goals, appropriately allocating resources such as vaccination campaigns, and …
Development Goals, appropriately allocating resources such as vaccination campaigns, and …
A geospatial platform for crowdsourcing green space area management using GIS and deep learning classification
S Puttinaovarat, P Horkaew - ISPRS International Journal of Geo …, 2022 - mdpi.com
Green space areas are one of the key factors in people's livelihoods. Their number and size
have a significant impact on both the environment and people's quality of life, including their …
have a significant impact on both the environment and people's quality of life, including their …
Exploiting deep learning and volunteered geographic information for mapping buildings in Kano, Nigeria
Buildings in the developing world are inadequately mapped. Lack of such critical geospatial
data adds unnecessary challenges to locating and reaching a large segment of the world's …
data adds unnecessary challenges to locating and reaching a large segment of the world's …
[HTML][HTML] Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas
Many cities in low-and medium-income countries (LMICs) are facing rapid unplanned
growth of built-up areas, while detailed information on these deprived urban areas (DUAs) is …
growth of built-up areas, while detailed information on these deprived urban areas (DUAs) is …
Detecting subpixel human settlements in mountains using deep learning: A case of the Hindu Kush Himalaya 1990–2020
The majority of future population growth in mountains will occur in small-and medium-sized
cities and towns and affect vulnerable ecosystems. However, mountain settlements are often …
cities and towns and affect vulnerable ecosystems. However, mountain settlements are often …
Integrating OpenStreetMap crowdsourced data and Landsat time-series imagery for rapid land use/land cover (LULC) mapping: Case study of the Laguna de Bay …
BA Johnson, K Iizuka - Applied Geography, 2016 - Elsevier
We explored the potential for rapid land use/land cover (LULC) mapping using time-series
Landsat satellite imagery and training data (for supervised classification) automatically …
Landsat satellite imagery and training data (for supervised classification) automatically …
Crowdsourcing-based indoor mapping using smartphones: A survey
Indoor map is a fundamental element of indoor location-based services (ILBS). However,
traditional indoor mapping techniques are labor-intensive and time-consuming. The …
traditional indoor mapping techniques are labor-intensive and time-consuming. The …
UVLens: Urban village boundary identification and population estimation leveraging open government data
Urban villages refer to the residential areas lagging behind the rapid urbanization process in
many developing countries. These areas are usually with overcrowded buildings, high …
many developing countries. These areas are usually with overcrowded buildings, high …
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