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Drone images for Deep Learning Image Analysis Tools to Identify Malaria Vector Habitats in West Africa

Name: Drone images for Deep Learning Image Analysis Tools to Identify Malaria Vector Habitats in West Africa
Creator: Miguel Nunez-del-Prado
Published: 2026-04-09T12:25:58+08:00

Mendeley Data2026-04-09 收录

疟疾媒介栖息地识别

遥感图像分类

数据链接：

https://data.mendeley.com/datasets/vvt6gvxgb6 数据链接链接失效反馈

官方服务：

资源简介：

Patches of drone images for different classes

应用场景：

提供机构：

Miguel Nunez-del-Prado

高分辨率土地覆盖

遥感图像分类

The GLC_FCS10 is a novel global land-cover product at 10 m with fine classification system containing 30 fine land-cover types, it is generated by a hierarchical land-cover mapping framework from Sent

NIAID Data Ecosystem100

Implementation of machine-learning classification in remote sensing: an applied review

遥感图像分类

机器学习方法应用

Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionalit

Taylor & Francis Group2023-06-01 更新40

Analysis of geo-spatiotemporal data using machine learning algorithms and reliability enhancement for urbanization decision support

城市扩张监测

遥感图像分类

We present systematic analyses of the temporal dynamics of the growth of Kumasi, the fastest growing city in Ghana using 20-year Landsat time-series data from 2000 to 2020 (with 1986 Landsat image as

Taylor & Francis Group2020-12-04 更新20

Classification accuracy of tree locations of IKONOS and WorldView-1 images.

遥感图像分类

植被识别精度评估

Classification accuracy of tree locations of IKONOS and WorldView-1 images.

NIAID Data Ecosystem40

DataCode.zip

城市功能区

遥感图像分类

These data are original data and code from "A SALT-ENSEMBLE LEARNING FRAMEWORK FOR URBAN FUNCTIONAL ZONES MAPPING USING REMOTE SENSING AND VGI DATA", including study area data, impervious surface data

NIAID Data Ecosystem20

Drone images for Deep Learning Image Analysis Tools to Identify Malaria Vector Habitats in West Africa

资源简介：

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