Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments

de Souza WM, Weaver SC. Effects of climate change and human activities on vector-borne diseases. Nat Rev Microbiol 2024.

Gibb R, Colon-Gonzalez FJ, Lan PT, Huong PT, Nam VS, Duoc VT, Hung DT, Dong NT, Chien VC, Trang LTT, et al. Interactions between climate change, urban infrastructure and mobility are driving dengue emergence in Vietnam. Nat Commun. 2023;14(1):8179.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Mondal N. The resurgence of dengue epidemic and climate change in India. Lancet. 2023;401(10378):727–8.

Article  PubMed  Google Scholar 

Xu Y, Zhou J, Liu T, Liu P, Wu Y, Lai Z, Gu J, Chen X. Assessing the risk of spread of Zika virus under current and future climate scenarios. Biosaf Health. 2022;4(3):193–204.

Article  Google Scholar 

Marques R, Kruger RF, Cunha SK, Silveira AS, Alves D, Rodrigues GD, Peterson AT, Jimenez-Garcia D. Climate change impacts on Anopheles (K.) cruzii in urban areas of Atlantic Forest of Brazil: challenges for malaria diseases. Acta Trop. 2021;224:106123.

Article  CAS  PubMed  Google Scholar 

Mora C, McKenzie T, Gaw IM, Dean JM, von Hammerstein H, Knudson TA, Setter RO, Smith CZ, Webster KM, Patz JA, et al. Over half of known human pathogenic diseases can be aggravated by climate change. Nat Clim Change. 2022;12(9):869–75.

Article  Google Scholar 

Samarasekera U. Climate change and malaria: predictions becoming reality. Lancet. 2023;402(10399):361–2.

Article  PubMed  Google Scholar 

Sanjeet B. Dengue outbreak in Peru affects adults and children. Lancet Infect Dis. 2023;23(9):e339.

Article  Google Scholar 

Fornace KM, Johnson E, Moreno M, Hardy A, Carrasco-Escobar G. Chap. 11 leveraging Earth observation data for surveillance of vector-borne diseases in changing environments. In. Leiden, The Netherlands: Wageningen Academic; 2023. pp. 319–46.

Google Scholar 

Malone JB, Bergquist R, Martins M, Luvall JC. Use of Geospatial Surveillance and Response systems for Vector-Borne diseases in the Elimination Phase. Trop Med Infect Dis 2019, 4(1).

Kalluri S, Gilruth P, Rogers D, Szczur M. Surveillance of Arthropod Vector-Borne Infectious diseases using Remote sensing techniques: a review. PLoS Pathog. 2007;3(10):e116.

Article  PubMed  PubMed Central  Google Scholar 

Zhao Q, Yu L, Du Z, Peng D, Hao P, Zhang Y, Gong P. An overview of the applications of Earth Observation Satellite Data: impacts and future trends. Remote Sens 2022, 14(8).

Wimberly MC, de Beurs KM, Loboda TV, Pan WK. Satellite observations and Malaria: New opportunities for Research and Applications. Trends Parasitol. 2021;37(6):525–37.

Article  PubMed  PubMed Central  Google Scholar 

Diuk-Wasser MABM, Sogoba N, Dolo G, Touré MB, Traoré SF, Taylor CE. Mapping rice field anopheline breeding habitats in Mali, West Africa, using landsat ETM + sensor data. Int J Remote Sens. 2004;25(2):359–76.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wilson M. Emerging and vector-borne diseases role of high spatial resolution and hyperspectral images in analyses and forecasts. J Geograph Syst. 2002;4:31–42.

Article  Google Scholar 

Hoek Spaans R, Drumond B, van Daalen KR, Rorato Vitor AC, Derbyshire A, Da Silva A, Lana RM, Vega MS, Carrasco-Escobar G, Sobral Escada MI, et al. Ethical considerations related to drone use for environment and health research: a scoping review protocol. PLoS ONE. 2024;19(1):e0287270.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Carrasco-Escobar G, Manrique E, Ruiz-Cabrejos J, Saavedra M, Alava F, Bickersmith S, Prussing C, Vinetz JM, Conn JE, Moreno M, et al. High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery. PLoS Negl Trop Dis. 2019;13(1):e0007105.

Article  PubMed  PubMed Central  Google Scholar 

Stanton MC, Kalonde P, Zembere K, Hoek Spaans R, Jones CM. The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control? Malar J. 2021;20(1):244.

Article  PubMed  PubMed Central  Google Scholar 

Hardy A, Makame M, Cross D, Majambere S, Msellem M. Using low-cost drones to map malaria vector habitats. Parasit Vectors. 2017;10(1):29.

Article  PubMed  PubMed Central  Google Scholar 

ZZapp. [https://www.zzappmalaria.com/post/zzapp-technology-how-it-works].

Trujillano F, Jimenez Garay G, Alatrista-Salas H, Byrne I, Nunez-del-Prado M, Chan K, Manrique E, Johnson E, Apollinaire N, Kouame Kouakou P et al. Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance. In: Remote Sensing vol. 15; 2023.

Wang C, Wang P, Ma N. A New Water Detection for Multispectral Images Based on Data Simulation and Random Forest. In: IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium: 17–22 July 2022 2022; 2022: 3191–3194.

Luo Y, Feng A, Li H, Li D, Wu X, Liao J, Zhang C, Zheng X, Pu H. New deep learning method for efficient extraction of small water from remote sensing images. PLoS ONE. 2022;17(8):e0272317.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Xu X, Zhang H, Ran Y, Tan Z. High-Precision Segmentation of Buildings with Small Sample Sizes Based on Transfer Learning and Multi-Scale Fusion. In: Remote Sensing vol. 15; 2023.

Zhang G, Roslan SNAb, Wang C, Quan L. Research on land cover classification of multi-source remote sensing data based on improved U-net network. Sci Rep. 2023;13(1):16275.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Maurício J, Domingues I, Bernardino J. Comparing Vision Transformers and Convolutional Neural Networks for Image Classification: A Literature Review. In: Applied Sciences vol. 13; 2023.

Bommasani R, Hudson DA, Adeli E, Altman RB, Arora S, von Arx S, Bernstein MS, Bohg J, Bosselut A, Brunskill E et al. On the opportunities and Risks of Foundation Models. CoRR 2021, abs/2108.07258.

Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo. W-Y: Segment Anything. 2023.

Wu Q, Prado Osco L. Samgeo: a Python package for segmenting geospatial data with the segment anything model (SAM). J Open Source Softw. 2023;8(89):5663.

Article  Google Scholar 

Roshanravan B, Kari E, Gilman RH, Cabrera L, Lee E, Metcalfe J, Calderon M, Lescano AG, Montenegro SH, Calampa C, et al. Endemic malaria in the Peruvian Amazon region of Iquitos. Am J Trop Med Hyg. 2003;69(1):45–52.

Article  PubMed  Google Scholar 

Byrne I, Chan K, Manrique E, Lines J, Wolie RZ, Trujillano F, Garay GJ, Del Prado Cortez MN, Alatrista-Salas H, Sternberg E et al. Technical Workflow Development for Integrating Drone Surveys and Entomological Sampling to Characterise Aquatic Larval Habitats of Anopheles funestus in Agricultural Landscapes in Côte d’Ivoire. J Environ Public Health 2021, 2021:3220244.

Kahamba NF, Okumu FO, Jumanne M, Kifungo K, Odero JO, Baldini F, Ferguson HM, Nelli L. Geospatial modelling of dry season habitats of the malaria vector, Anopheles Funestus, in south-eastern Tanzania. Parasites Vectors. 2024;17(1):38.

Article  PubMed  PubMed Central  Google Scholar 

Byrne I, Aure W, Manin B, Vythilingam I, Ferguson HM, Drakeley C, Chua T, Fornace K. Environmental and spatial risk factors for the larval habitats of Plasmodium knowlesi vectors in Sabah, Malaysian Borneo. Sci Rep. 2021;11(1):11810.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Fornace KM, Drakeley CJ, William T, Espino F, Cox J. Mapping infectious disease landscapes: unmanned aerial vehicles and epidemiology. Trends Parasitol. 2014;30(11):514–9.

Article  PubMed  Google Scholar 

Liu Y, Zhang Y, Wang Y, Hou F, Yuan J, Tian J, Zhang Y, Shi Z, Fan J, He Z. A Survey of Visual transformers. IEEE Trans Neural Networks Learn Syst 2023:1–21.

Liu S, Zeng Z, Ren T, Li F, Zhang H, Yang J, Li C, Yang J, Su H, Zhu J et al. Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection. 2023.

QGIS.org. QGIS Geographic Information System. In., 3.32 edn. QGIS Association; 2024.

OpenAI ChatGPT-4. In.; 2024.

Osco LP, Wu Q, de Lemos EL, Gonçalves WN, Ramos APM, Li J, Marcato J. The segment anything model (SAM) for remote sensing applications: from zero to one shot. Int J Appl Earth Obs Geoinf. 2023;124:103540.

Google Scholar 

Babu GJ, Bose A. Bootstrap confidence intervals. Stat Probab Lett. 1988;7(2):151–60.

Article  Google Scholar 

Zhang J, Yang X, Jiang R. W. S, L. Z: RSAM-Seg: A SAM-based Approach with Prior Knowledge Integration for Remote Sensing Image Semantic Segmentation. arXiv 2024.

Ren S, Luzi F, Lahrichi S, Kassaw K, Collins LM, Bradbury K, Malof JM. Segment Anything, From Space? Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2024:8355–8365.

Kucharczyk M, Hugenholtz CH. Remote sensing of natural hazard-related disasters with small drones: global trends, biases, and research opportunities. Remote Sens Environ. 2021;264:112577.

Article  Google Scholar 

Sirko W, Kashubin S, Ritter M, Annkah A, Salah Y, Bouchareb E, Dauphin Y, Keysers D, Neumann M, Cisse M et al. Continental-Scale Building Detection from High Resolution Satellite Imagery. arXiv 2021.

Mayladan A, Nasrallah H, Moughnieh H, Shukor M, Ghandour AJ. Zero-Shot Refinement of Buildings’ Segmentation Models using SAM. arXiv 2024.

Zhang Y, Shen Z, Jiao R. Segment anything model for medical image segmentation: current applications and future directions. Comput Biol Med. 2024;171:108238.

Article  PubMed  Google Scholar 

Carrasco-Escobar G, Moreno M, Fornace K, Herrera-Varela M, Manrique E, Conn JE. The use of drones for mosquito surveillance and control. Parasit Vectors. 2022;15(1):473.

Article  PubMed  PubMed Central  Google Scholar 

Hardy A, Oakes G, Hassan J, Yussuf Y. Improved use of Drone Imagery for Malaria Vector Control through Technology-assisted Digitizing (TAD). Remote Sens 2022, 14(2).

Valdez-Delgado KM, Moo-Llanes DA, Danis-Lozano R, Cisneros-Vázquez LA, Flores-Suarez AE, Ponce-García G, Medina-De la Garza CE, Díaz-González EE, Fernández-Salas I. Field effectiveness of drones to identify potential aedes aegypti breeding sites in household environments from tapachula a dengue-endemic city in southern mexico insects. 2021;12(8):663. https://doi.org/10.3390/insects12080663.

Passos WL, Araujo GM, De Lima AA, Netto SL, Da Silva EA. Automatic detection of aedes aegypti breeding grounds based on deep networks with spatio-temporal consistency computers environment and urban systems. 2022;93:101754. https://doi.org/10.1016/j.compenvurbsys.2021.101754.

Steven MC, Solomon PD, Arumugam P, Rasali R, Dominic AC, Ideris HM, Marius DF. Short report: unmanned aerial vehicle for wide area larvicide spraying (WALS) using Vectobac® WG at Kota Kinabalu Sabah. J Infect Dev Ctries. 2024;18(02):299–302. https://doi.org/10.3855/jidc.18292.

Muñiz-Sánchez V, Valdez-Delgado KM, Hernandez-Lopez FJ, Moo-Llanes DA, González-Farías G, Danis-Lozano R. Use of Unmanned Aerial vehicles for building a House Risk Index of Mosquito-Borne viral diseases. Machines. 2022;10(12):1161.

Comments (0)

No login
gif