E Kutchartt, JR González-Olabarria, A Trasobares, S de-Miguel, A Cardil, B Botequim, V Vassilev, P Palaiologou, M Rogai, F Pirotti
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引用次数: 0
Abstract
We present a browser-based App for smartphones that is freely available to end-users for collecting geotagged and oriented photos depicting vegetation biomass and fuel characteristics. Our solution builds on advantages of smartphones, allowing their use as easy sensors to collect data by imaging forest ecosystems. The strength and innovation of the proposed solution is based on the following points: (i) using a low memory footprint App, streaming images and data with as little data-volume and memory as needed; (ii) using JavaScript APIs that can be launched from both a browser or as an installed App, as it applies features such as service workers and Progressive Web App; (iii) storing both image and survey data (geolocation and sensor orientation) internally to the device on an indexed database, and synchronizing the data to a cloud-based server when the smartphone is online and when all other safety tests have been successfully passed. The goal is to achieve properly positioned and oriented photos that can be used as training and testing data for future estimation of the surface fuel types based on automatic segmentation and classification via Machine Learning and Deep Learning.
我们提出了一个基于浏览器的智能手机应用程序,它是免费提供给终端用户收集地理标记和定向照片描绘植被生物量和燃料特性。我们的解决方案建立在智能手机的优势之上,允许它们作为简单的传感器使用,通过成像森林生态系统来收集数据。所提出的解决方案的优势和创新基于以下几点:(i)使用低内存占用的应用程序,流式图像和数据与尽可能少的数据量和内存的需要;(ii)使用可以从浏览器启动或作为已安装的应用程序启动的JavaScript api,因为它应用了service worker和Progressive Web App等功能;㈢在设备内部将图像和调查数据(地理位置和传感器方向)存储在一个索引数据库中,并在智能手机在线并成功通过所有其他安全测试时将数据同步到基于云的服务器。目标是获得正确定位和定向的照片,这些照片可以作为训练和测试数据,用于未来基于机器学习和深度学习的自动分割和分类来估计表面燃料类型。
期刊介绍:
The journal encompasses a broad range of research aspects concerning forest science: forest ecology, biodiversity/genetics and ecophysiology, silviculture, forest inventory and planning, forest protection and monitoring, forest harvesting, landscape ecology, forest history, wood technology.