Journal: International Journal of Digital Earth

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Taylor & Francis

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1753-8947
1753-8955

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2008 - 200912020 - 20245
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Publications 1 - 6 of 6
  • Reality-based generation of virtual environments for digital earth
    Item type: Journal Article
    Gruen, A. (2008)
    International Journal of Digital Earth
  • Collecting volunteered geographic information from the Global Navigation Satellite System (GNSS): experiences from the CAMALIOT project
    Item type: Journal Article
    See, Linda; Soja, Benedikt; Klopotek, Grzegorz; et al. (2023)
    International Journal of Digital Earth
    Raw observations (carrier-phase and code observations) from the Global Navigation Satellite System (GNSS) can now be accessed from Android mobile phones (Version 7.0 onwards). This paves the way for GNSS data to be utilized for low-cost precise positioning or in ionospheric or tropospheric applications. This paper presents results from data collection campaigns using the CAMALIOT mobile app. In the first campaign, 116.3 billion measurements from 11,828 mobile devices were collected from all continents. Although participation decreased during the second campaign, data are still being collected globally. In this contribution, we demonstrate the potential of volunteered geographic information (VGI) from mobile phones to fill data gaps in geodetic station networks that collect GNSS data, e.g. in Brazil, but also how the data can provide a denser set of observations than current networks in countries across Europe. We also show that mobile phones capable of dual-frequency reception, which is an emerging technology that can provide a richer source of GNSS data, are contributing in a substantial way. Finally, we present the results from a survey of participants to indicate that participation is diverse in terms of backgrounds and geography, where the dominant motivation for participation is to contribute to scientific research.
  • Floating in the air: forecasting allergenic pollen concentration for managing urban public health
    Item type: Review Article
    Zhu, Xiaoyu; Ma, Xuanlong; Zhang, Zhengyang; et al. (2024)
    International Journal of Digital Earth
    The presence of airborne allergenic pollen causes a variety of immune reactions and respiratory diseases, threatening human life in severe cases. Climate change is exacerbating the allergenic pollen-induced health risks and adding a significant economic burden to societies. Despite the pressing threats, vital health-related information is not available to the public to date, and the reshaping of future geographic allergenic pollen patterns remains unknown. To help establish a critical allergenic pollen forecasting capacity, a systematic review was conducted and three promising future directions were identified: (1) resolving heterogeneous urban plant species distribution and phenology using fine-resolution satellite constellations; (2) acquiring ancillary information about allergenic pollen and patient symptoms from emerging geospatial big data, such as social media; (3) deciphering the coupled effect of climate change and urbanization on future geographic patterns and phenology of allergenic species. On this basis, we recommend an optimized workflow that combines real-time pollen monitoring networks with high-resolution vegetation information and weather forecast systems, comprehensively considering the production and diffusion process of pollen to establish advanced prediction models. By focusing on critical knowledge gaps, this review provides much needed insight to propel the allergenic pollen forecasting research and eventually benefit the management of urban public health.
  • FORSAT: a 3D forest monitoring system for cover mapping and volumetric 3D change detection
    Item type: Journal Article
    Stylianidis, Efstratios; Akca, Devrim; Poli, Daniela; et al. (2020)
    International Journal of Digital Earth
    A 3D forest monitoring system, called FORSAT (a satellite very high resolution image processing platform for forest assessment), was developed for the extraction of 3D geometric forest information from very high resolution (VHR) satellite imagery and the automatic 3D change detection. FORSAT is composed of two complementary tasks: (1) the geometric and radiometric processing of satellite optical imagery and digital surface model (DSM) reconstruction by using a precise and robust image matching approach specially designed for VHR satellite imagery, (2) 3D surface comparison for change detection. It allows the users to import DSMs, align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes (together with precision values) between epochs. FORSAT is a single source and flexible forest information solution, allowing expert and non-expert remote sensing users to monitor forests in three and four (time) dimensions. The geometric resolution and thematic content of VHR optical imagery are sufficient for many forest information needs such as deforestation, clear-cut and fire severity mapping. The capacity and benefits of FORSAT, as a forest information system contributing to the sustainable forest management, have been tested and validated in case studies located in Austria, Switzerland and Spain. © 2019 Informa UK Limited, trading as Taylor & Francis Group.
  • The role of machine intelligence in photogrammetric 3D modeling - an overview and perspectives
    Item type: Review Article
    Qin, Rongjun; Grün, Armin (2021)
    International Journal of Digital Earth
    The process of modern photogrammetry converts images and/or LiDAR data into usable 2D/3D/4D products. The photogrammetric industry offers engineering-grade hardware and software components for various applications. While some components of the data processing pipeline work already automatically, there is still substantial manual involvement required in order to obtain reliable and high-quality results. The recent development of machine learning techniques has attracted a great attention in its potential to address complex tasks that traditionally require manual inputs. It is therefore worth revisiting the role and existing efforts of machine learning techniques in the field of photogrammetry, as well as its neighboring field computer vision. This paper provides an overview of the state-of-the-art efforts in machine learning in bringing the automated and 'intelligent' component to photogrammetry, computer vision and (to a lesser degree) to remote sensing. We will primarily cover the relevant efforts following a typical 3D photogrammetric processing pipeline: (1) data acquisition (2) geo-referencing/interest point matching (3) Digital Surface Model generation (4) semantic interpretations, followed by conclusions and our insights.
  • 3D dataset generation using virtual reality for forest biodiversity
    Item type: Journal Article
    Fol, Cyprien R.; Shi, Nianfang; Overney, Normand; et al. (2024)
    International Journal of Digital Earth
    While forest biodiversity faces a concerning decline, modern technology presents promising avenues for mitigation. However, a critical gap persists in reconciling ecological knowledge with the technical expertise required to use state-of-the-art technologies in 3D data classification. Currently, one main issue is the scarcity of 3D datasets for biodiversity, particularly within the context of machine learning applications. Unlike the straightforward classification of human-made structures, forest environments are uniquely intricate and nuanced due to its inherently complex nature. This study addresses this challenge by introducing a fully automated pipeline for tree stem 3D point cloud segmentation, focussing on a biodiversity indicator: tree-related microhabitats (TreMs). Furthermore, our research advances the field by demonstrating that machine learning models trained with labels generated by our proposed virtual reality (VR) method, Labelling Flora, yield predictions statistically similar to the traditional desktop-based labelling methods. This implies that existing 3D datasets could be augmented using the more rapid approach of VR labelling. Additionally, the findings of this paper demonstrate the potential integration of VR and immersive technology into the 3D labelling workflow, facilitating a quicker and more intuitive labelling process. This could empower users, who are non-familiar with 3D modelling, to contribute their expertise to the segmentation process.
Publications 1 - 6 of 6