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Item type:Publication, Lasers Without Lost Cities: Using Drone Lidar to Capture Architectural Complexity at Kuelap, Amazonas, Peru(Taylor and Francis Ltd., 2020-02-20)We report the results of drone lidar survey at a high-elevation archaeological site in the Chachapoyas region of Peruvian Amazonia. Unlike traditional airborne remote sensing, drone lidar produces very high-density measurements at a wide range of scan angles by operating at low altitudes and slow flight speeds. These measurements can resolve near vertical surfaces and novel dimensions of variability in architectural datasets. We show in a case study at Kuelap that the number of detected structures almost exactly matches the number reported from previous ground level surveys, and we use these data to quantify the relative circularity and size frequency distribution of architectural structures. We demonstrate variability in domestic architecture that was obscured in previous models produced using low-resolution remote sensing. Spatial analysis of these attributes produces new hypotheses about the site’s construction history and social organization. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 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(Hindawi Limited, 2021-01-01)Land-use practices such as agriculture can impact mosquito vector breeding ecology, resulting in changes in disease transmission. The typical breeding habitats of Africa’s second most important malaria vector Anopheles funestus are large, semipermanent water bodies, which make them potential candidates for targeted larval source management. This is a technical workflow for the integration of drone surveys and mosquito larval sampling, designed for a case study aiming to characterise An. funestus breeding sites near two villages in an agricultural setting in Côte d’Ivoire. Using satellite remote sensing data, we developed an environmentally and spatially representative sampling frame and conducted paired mosquito larvae and drone mapping surveys from June to August 2021. To categorise the drone imagery, we also developed a land cover classification scheme with classes relative to An. funestus breeding ecology. We sampled 189 potential breeding habitats, of which 119 (63%) were positive for the Anopheles genus and nine (4.8%) were positive for An. funestus. We mapped 30.42 km2 of the region of interest including all water bodies which were sampled for larvae. These data can be used to inform targeted vector control efforts, although its generalisability over a large region is limited by the fine-scale nature of this study area. This paper develops protocols for integrating drone surveys and statistically rigorous entomological sampling, which can be adjusted to collect data on vector breeding habitats in other ecological contexts. Further research using data collected in this study can enable the development of deep-learning algorithms for identifying An. funestus breeding habitats across rural agricultural landscapes in Côte d’Ivoire and the analysis of risk factors for these sites. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Warm Start Trajectory Reinforcement Learning for Multi-Mode Project Scheduling under Resource Constraints in UAV Manufacturing(RELX Group (Netherlands), 2025-01-01)1