aerial-image-analysis

CGIAR project - Earth Observation Data

Research objectif

Estimate Funestus mosquito amount based on satellite and drone images

Literature

EO

• Asare, E. O., Tompkins, A. M., & Bomblies, A. (2016). A regional model for malaria vector developmental habitats evaluated using explicit, pond-resolving surface hydrology simulations. PLoS One, 11(3), e0150626.
• Brandt, M., Tucker, C. J., Kariryaa, A., Rasmussen, K., Abel, C., Small, J., ... & Fensholt, R. (2020). An unexpectedly large count of trees in the West African Sahara and Sahel. Nature, 587(7832), 78-82.
• Hardy, A. J., Gamarra, J. G., Cross, D. E., Macklin, M. G., Smith, M. W., Kihonda, J., ... & Thomas, C. J. (2013). Habitat hydrology and geomorphology control the distribution of malaria vector larvae in rural Africa. PLoS One, 8(12), e81931.
• Moshe, Z., Metzger, A., Elidan, G., Kratzert, F., Nevo, S., & El-Yaniv, R. (2020). Hydronets: Leveraging river structure for hydrologic modeling. arXiv preprint arXiv:2007.00595.
• Prošek, J., Gdulová, K., Barták, V., Vojar, J., Solský, M., Rocchini, D., & Moudrý, V. (2020). Integration of hyperspectral and LiDAR data for mapping small water bodies. International Journal of Applied Earth Observation and Geoinformation, 92, 102181.
• Smith, M. W., Willis, T., Alfieri, L., James, W. H. M., Trigg, M. A., Yamazaki, D., ... & Thomas, C. J. (2020). Incorporating hydrology into climate suitability models changes projections of malaria transmission in Africa. Nature communications, 11(1), 1-9.
• Topp, S. N., Pavelsky, T. M., Jensen, D., Simard, M., & Ross, M. R. (2020). Research trends in the use of remote sensing for inland water quality science: Moving towards multidisciplinary applications. Water, 12(1), 16339.

Image Analisys

• Chen, Y., Fan, R., Yang, X., Wang, J., & Latif, A. (2018). Extraction of urban water bodies from high-resolution remote-sensing imagery using deep learning. Water, 10(5), 585.
• Minakshi, M., Bhuiyan, T., Kariev, S., Kaddumukasa, M., Loum, D., Stanley, N. B., ... & Jacob, B. G. (2020). High-accuracy detection of malaria mosquito habitats using drone-based multispectral imagery and Artificial Intelligence (AI) algorithms in an agro-village peri-urban pastureland intervention site (Akonyibedo) in Unyama SubCounty, Gulu District, Northern Uganda. Journal of Public Health and Epidemiology, 12(3), 202-217.
• Song, S., Liu, J., Liu, Y., Feng, G., Han, H., Yao, Y., & Du, M. (2020). Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery. Sensors, 20(2), 397.

Data sources

• Bukina Faso
• Code d'Yvoire

Installation

Clone the source coder.

git clone https://github.com/healthinnovation/aerial-image-analysis.git

Repository structure

To be done