Mapping human settlements with higher accuracy and less volunteer efforts by combining crowdsourcing and deep learning

B Herfort, H Li, S Fendrich, S Lautenbach, A Zipf - Remote Sensing, 2019 - mdpi.com
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 …

Deep learning from multiple crowds: A case study of humanitarian mapping

J Chen, Y Zhou, A Zipf, H Fan - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
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 …

[HTML][HTML] A census from heaven: Unraveling the potential of deep learning and Earth Observation for intra-urban population mapping in data scarce environments

S Georganos, S Hafner, M Kuffer, C Linard… - International Journal of …, 2022 - Elsevier
Urban population distribution maps are vital elements for monitoring the Sustainable
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 …

Exploiting deep learning and volunteered geographic information for mapping buildings in Kano, Nigeria

J Yuan, PK Roy Chowdhury, J McKee, HL Yang… - Scientific data, 2018 - nature.com
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 …

[HTML][HTML] Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas

A Abascal, I Rodríguez-Carreño, S Vanhuysse… - … , environment and urban …, 2022 - Elsevier
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 …

Detecting subpixel human settlements in mountains using deep learning: A case of the Hindu Kush Himalaya 1990–2020

THK Chen, B Pandey, KC Seto - Remote Sensing of Environment, 2023 - Elsevier
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 …

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 …

Crowdsourcing-based indoor mapping using smartphones: A survey

B Zhou, W Ma, Q Li, N El-Sheimy, Q Mao, Y Li… - ISPRS Journal of …, 2021 - Elsevier
Indoor map is a fundamental element of indoor location-based services (ILBS). However,
traditional indoor mapping techniques are labor-intensive and time-consuming. The …

UVLens: Urban village boundary identification and population estimation leveraging open government data

L Chen, C Lu, F Yuan, Z Jiang, L Wang… - Proceedings of the …, 2021 - dl.acm.org
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 …