Resultados de búsqueda - "Cloud computing"

Limitar resultados
  1. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing por Xiong, Jun, Thenkabail, Prasad S., Gumma, Murali K., Teluguntla, P., Poehnelt, J., Congalton, Russell, Yadav, K., Thau, D.

    Publicado 2017
    “…The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived cropland layers for these years (ACL2003 to ACL2014). …”
    Enlace del recurso
    Journal Article
  2. Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure por Gokool, S., Mahomed, M., Brewer, K., Naiken, V., Clulow, A., Sibanda, M., Mabhaudhi, Tafadzwanashe

    Publicado 2024
    “…Furthermore, advances in geospatial cloud computing have opened new and exciting possibilities in the remote sensing arena. …”
    Enlace del recurso
    Journal Article
  3. A systematic review of existing early warning systems’ challenges and opportunities in cloud computing early warning systems por Agbehadji, I. E., Mabhaudhi, Tafadzwanashe, Botai, J., Masinde, M.

    Publicado 2023
    “…This paper assessed existing EWS challenges and opportunities in cloud computing through the PSALSAR framework for systematic literature review and meta-analysis. …”
    Enlace del recurso
    Journal Article
  4. Advancing wetland mapping in Argentina: a probalitistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring por Navarro, María Fabiana, Calamari, Noelia Cecilia, Navarro, Carlos Saúl, Enriquez, Andrea Soledad, Mosciaro, Maria Jesus, Saucedo, Griselda Isabel, Barrios, Raúl Ariel, Curcio, Matías Hernán, Dieta, Victorio, Garcia Martinez, Guillermo Carlos, Iturralde Elortegui, Maria Del Rosario Ma, Michard, Nicole Jacqueline, Paredes, Paula Natalia, Umaña, Fernando, Alday Poblete, Silvina Esther, Pezzola, Nestor Alejandro, Vidal, Claudia, Winschel, Cristina Ines, Albarracin Franco, Silvia, Behr, Santiago Javier, Cianfagna, Francisco A., Cremona, Maria Victoria, Alvarenga, Fernando Agustin, Perucca, Alba Ruth, Lopez, Astor Emilio, Miranda, Federico Waldemar, Kurtz, Ditmar Bernardo

    Publicado 2025
    “…This study addresses these challenges by presenting a probabilistic wetland distribution map for Argentina, inte­ grating 20 years of satellite imagery with machine learning and cloud computing technologies. Our approach introduces a comprehensive set of biophysical indices, enabling the identification of key wetland characteristics: 1) permanent or temporal surface water presence; 2) water-adapted vegetation phenology; and 3) geo­ morphology conducive to water accumulation. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Artículo
  5. EO-WEF: a earth observations for water, energy, and food nexus geotool for spatial data visualization and generation por Kiala, Z., Jewitt, G., Senzanje, A., Mutanga, O., Dube, T., Mabhaudhi, Tafadzwanashe

    Publicado 2022
    “…Furthermore, the advent of cloud computing has made possible the processing of massive information. …”
    Enlace del recurso
    Capítulo de libro
  6. The greenhouse gas emissions estimates of hydropower reservoirs in Vietnam using G-res Tool: bridging climate change mitigation with sustainability frameworks por Ghosh, Surajit, De Sarkar, K., Chowdhury, A., Holmatov, Bunyod, Rajakaruna, Punsisi

    Publicado 2023
    “…Considering the use of cloud computing in GHG quantification can support global efforts to mitigate climate change and advance the development of hydropower systems into more sustainable global infrastructure. …”
    Enlace del recurso
    Informe técnico
  7. Sustainable information technology practice in libraries por Ayinla, B.A., Aramide, K.A.

    Publicado 2023
    “…Similarly, it used recent research in cloud computing to explain some environmental sustainability issues related to information technology in libraries.…”
    Enlace del recurso
    Capítulo de libro
  8. Internet of things: Applications to developing country agriculture sector por Lalitha, A., Babu, Suresh Chandra, Purnima, K.S.

    Publicado 2018
    “…The sensor data visualization for agriculture has a great opportunity for mobile technology. Cloud computing offers several applications in the field of agriculture with limited infrastructure and costs. …”
    Enlace del recurso
    Journal Article
  9. Assessment of land degradation in semiarid Tanzania using multiscale remote sensing datasets to support sustainable development goal 15.3 por Reith, J., Ghazaryan, G., Muthoni, Francis K., Dubovyk, O.

    Publicado 2021
    “…It incorporates freely available datasets such as Landsat time series and customized land cover and uses open-source software and cloud-computing. Further, we compared our results of the LD assessment based on the adopted high-resolution data and methodology (AM) with the default medium-resolution data and methodology (DM) suggested by the United Nations Convention to Combat Desertification. …”
    Enlace del recurso
    Journal Article
  10. Assessment of land degradation in semi-arid Tanzania: Using remote sensing to inform the Sustainable Development Goal 15.3 por Reith, J.A.

    Publicado 2020
    “…It incorporates freely available datasets like Landsat and uses open-source software in interplay with cloud-computing. Human-induced LD was assessed using the Normalized Difference Vegetation Index (NDVI) correcting it for precipitation variability with the Rain Use Efficiency (RUE). …”
    Enlace del recurso
    Tesis
  11. Automatization and evaluation of a remote sensing-based indicator for wetland health assessment in East Africa on national and local scales por Steinbach, S., Hentschel, E., Hentze, K., Rienow, A., Umulisa, V., Zwart, Sander J., Nelson, A.

    Publicado 2023
    “…We developed a new and automated approach for WUI calculation that is implemented in the Google Earth Engine (GEE) cloud computing environment. Its automatic calculation, use of regular Sentinel-2 derived time series, and automatic cloud and cloud shadow masking renders WUI applicable for wetland management and produces high quality results with minimal user requirements, even under cloudy conditions. …”
    Enlace del recurso
    Journal Article

Herramientas de búsqueda: