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Over the past decades, the successful employment of aerial and satellite imagery and remote sensing (RS) data has been very diverse and important in many scientific fields. Firstly, a brief review of RS history is presented in section one. Then, basic properties, which are also challenges, of RS big data are concisely discussed. Volume, variety and...
Citations
... Sensor-based monitoring systems use a network of sensors placed along the river to collect data on water levels, discharge, and rainfall, among other parameters [12]. These data are transmitted in real-time to a central server for analysis and integration with other data sources, such as satellite imagery and meteorological data [13]. The use of sensors provides a cost-effective and efficient solution compared to traditional manual monitoring methods [14]. ...
Reliable and accurate flood prediction in poorly gauged basins is challenging due to data scarcity, especially in developing countries where many rivers remain insufficiently monitored. This hinders the design and development of advanced flood prediction models and early warning systems. This paper introduces a multi-modal, sensor-based, near-real-time river monitoring system that produces a multi-feature data set for the Kikuletwa River in Northern Tanzania, an area frequently affected by floods. The system improves upon existing literature by collecting six parameters relevant to weather and river flood detection: current hour rainfall (mm), previous hour rainfall (mm/h), previous day rainfall (mm/day), river level (cm), wind speed (km/h), and wind direction. These data complement the existing local weather station functionalities and can be used for river monitoring and extreme weather prediction. Tanzanian river basins currently lack reliable mechanisms for accurately establishing river thresholds for anomaly detection, which is essential for flood prediction models. The proposed monitoring system addresses this issue by gathering information about river depth levels and weather conditions at multiple locations. This broadens the ground truth of river characteristics, ultimately improving the accuracy of flood predictions. We provide details on the monitoring system used to gather the data, as well as report on the methodology and the nature of the data. The discussion then focuses on the relevance of the data set in the context of flood prediction, the most suitable AI/ML-based forecasting approaches, and highlights potential applications beyond flood warning systems.
... Sensor-based monitoring systems use a 82 network of sensors placed along the river to collect data on water levels, discharge, and 83 rainfall, among other parameters [7]. This data is transmitted in real-time to a central 84 server for analysis and integration with other data sources, such as satellite imagery and 85 meteorological data [8]. The use of sensors provides a cost-effective and efficient solution 86 compared to traditional manual monitoring methods [9]. ...
Reliable and accurate flood prediction is a challenging task in poorly gauged basins due to data scarcity. Data is an essential component of any AI/ML model today, and the performance of such models hugely depends on the availability of sufficient amount of trusted, representative data. However, unlike a few well-studied rivers, most of the rivers in developing countries are still insufficiently monitored, which significantly hinges the design and development of advanced flood prediction models and early warning systems. This paper presents a multi-modal, sensor-based and near-real time river monitoring system to produce a multi-feature data set for the Kikuletwa river in Northern Tanzania, an area that heavily suffers from frequent floods. Our deployed system, which gather information about river depth levels and weather at several locations, aims at widening the ground truth of the river characteristics and eventually improve the accuracy of flood predictions. We provide details on the monitoring system used to gather the data as well as report on the methodology and the nature of the data. Finally, we present the relevance of the data set in the context of flood prediction, discussing the most suitable AI/ML-based forecasting approaches, while also highlighting some applications of the data set beyond flood warning systems.
... Sensor-based monitoring systems use a network of sensors placed along the river to collect data on water levels, discharge, and rainfall, among other parameters [12]. These data are transmitted in real-time to a central server for analysis and integration with other data sources, such as satellite imagery and meteorological data [13]. The use of sensors provides a cost-effective and efficient solution compared to traditional manual monitoring methods [14]. ...
Reliable and accurate flood prediction is a challenging task in poorly gauged basins due to data scarcity. Data is an essential component of any AI/ML model today, and the performance of such models hugely depends on the availability of sufficient amount of trusted, representative data. However, unlike a few well-studied rivers, most of the rivers in developing countries are still insufficiently monitored, which significantly hinges the design and development of advanced flood prediction models and early warning systems. This paper presents a multi-modal, sensor-based and near-real time river monitoring system to produce a multi-feature data set for the Kikuletwa river in Northern Tanzania, an area that heavily suffers from frequent floods. Our deployed system, which gather information about river depth levels and weather at several locations, aims at widening the ground truth of the river characteristics and eventually improve the accuracy of flood predictions. We provide details on the monitoring system used to gather the data as well as report on the methodology and the nature of the data. Finally, we present the relevance of the data set in the context of flood prediction, discussing the most suitable AI/ML-based forecasting approaches, while also highlighting some applications of the data set beyond flood warning systems.
... With the advancement of remote sensing technology, the amount of data obtained by satellites is increasing dramatically. Also, with the increasing requirement for accurate and up-to-date environmental information for monitoring, massive multispectral datasets are exploited for processing Li et al. 2021;Chen et al. 2022;Salazar Loor and Fdez-Arroyabe 2019), and (Madhukar 2019). ...
In recent years, one of the biggest concerns of researchers has been environmental knowledge. This concern can be resolved by collecting and processing remote sensing data in the shortest possible time and cost with the highest accuracy and efficiency. In remote sensing, various types of satellite data are processed for different purposes and applications. In this process, data storage and processing methods, resource management, scalability, performance improvement, and efficiency are among the issues and challenges in this scope. This paper presents a service-oriented framework using big data and parallel processing in remote sensing to address these challenges. The proposed framework provides scalability, flexibility, and generalization without dependency on specific data or processing techniques. In addition, it provides reasonable results to quality criteria such as response time, efficiency, and performance. The evaluation results of the proposed framework show the effectiveness of the framework for various types of analysis of remote sensing data with acceptable accuracy.
... This data is big data. This increase also applies to spatial data, which have particularly gained importance due to mobile devices equipped with geolocations [2,3,4,5] constellations of various geostationary and non-geostationary satellites [6,7,8,9] Volunteer Geographic Information [10], or finally Global Positioning System [11]. It should also be added that spatial data have a decidedly different character than alphanumeric information, hence the increasing amount of this type of data forces the use of a dedicated approach to handle it [12, 13, and 14].To respond to these new requirements GIS Cloud is now being adopted. ...
Cloud computing technology is one of the most important technology in information systems (IS) today. It is an important alternative that ensures powerful data processing, storage and exchange. And as a result of the emergence of multiple sources of spatial data and their tremendous growth and the expansion of areas of use of Geospatial Information Systems (GIS), it became necessary to integrate these tow technologies to achieve the optimum benefit of these technologies to make Geospatial Information Systems capable of accommodating the large, rapid, and diverse amounts of spatial data available nowadays. Cloud computing is a new paradigm in Geographic Information Systems. Although there are many publications related to cloud computing in Geographic Information System, there is no systematic review of current research taxonomies .The purpose of this paper is to conduct a survey on GIS Cloud Computing. It addresses the different GIS Cloud frameworks or architectures. The search for articles carried out in general academic databases including the Scopus database, Web of Science Core Collection, and Google Scholar Citations. Retried articles are analysed according to inclusion and exclusion criteria, finally articles selected for review.
... Among them, satellite imaginary volume amount grows rapidly with advancement of technologies. For instance, in 2019, Sentinel (Sentinel-1, Sentinel-2, Sentinel-3), Landsat-7, Landsat-8, MODIS produce around 5 PB data [1], [2], [3]. For this reason, some solutions are proposed for building the platform for storing, managing, and processing EO data such as Google Earth Engine (GEE) [4], Sentinel Hub [5], Open Data Cube (ODC) [6], OpenEO [7], etc.. GEE is a platform that provides petabytes of satellite imagery and large-scale applicability for analysis. ...
... • Remove low quality data: It is the masking operation to set values to missing data or cloudy cells. The SCL (scene classification) band is used to check if a cell contains no data, saturated, cloud probability, or vegetation i.e., the cell value is in range of 0, 1,4,8,9,10. It can be implemented by invoking function rf local is in[0, 1,4,8,9,10]. ...
... The SCL (scene classification) band is used to check if a cell contains no data, saturated, cloud probability, or vegetation i.e., the cell value is in range of 0, 1,4,8,9,10. It can be implemented by invoking function rf local is in[0, 1,4,8,9,10]. ...
—Earth data, collected from many sources such as remote sensing imagery, social media, and sensors, are growing tremendously. Among them, satellite imagery which play an important roles for monitoring environment and natural changes are increased exponentially in term of both volume and speed. This paper introduces an approach to managing and analyzing such data sources based on Apache Hadoop and RasterFrames. First, it presents the architecture and the general flow of the proposed distributed framework. Based on this, we can implement and perform efficient computations on a big data in parallel without moving data to the center computer which might lead to network congestion. Finally, the paper presents a case study that analyzes the water surface of a Vietnam region using the proposed platform. © 2020 Science and Information Organization. All rights reserved.
“Harmony in Nature” offers a thorough examination of biodiversity conservation methods. It serves as a guide for scientists, policymakers, and enthusiasts. Beginning with the urgent need for conservation, it emphasizes ecosystems’ role in sustaining life. The book covers traditional and modern assessment methods, including field studies and advanced technologies like satellite imagery. It stresses integrating indigenous knowledge, citizen science, and community-based approaches. Adaptive management, policy frameworks, and global collaborations are discussed. Case studies illustrate successful initiatives and lessons learned. Ethical considerations and sustainability are integrated throughout. The book concludes with a call to action for global biodiversity preservation. It aims to be a valuable resource for academia, practitioners, and policymakers, fostering understanding of essential methodologies for successful conservation amidst environmental challenges.
This chapter briefly covers the five core dimensions of remote sensing big data, that is, volume, variety, velocity, veracity, and value. There are also other Vs to be explored, like Visualization for effectively high-dimensional visuals and exploration (Huang et al. J Integrat Agric 17:1915–1931, 2018), Volatility for data time-sensitivity (Antunes et al. GIScience Remote Sens 56:536–553, 2019), Validity for the exploration of hidden relationships among elements (Shelestov et al. Front Earth Sci 5 2017), and Viscosity for the complexity (Manogaran and Lopez Int J Biomed Eng Technol 25:182, 2017). Remote sensing big data may cover as many Vs as other big data (Khan et al. Proceedings of the International Conference on Omni-Layer Intelligent Systems - COINS ‘19. ACM Press, Crete, Greece, 2019).KeywordsRemote sensing big data Volume Variety Velocity Veracity Value
Data are the fundamental building blocks to conduct scientific studies that seek to understand natural phenomena in space and time. The notion of data processing is ubiquitous and nearly operates in any project that requires gaining insight from the data. The increasing availability of information sources, data formats and download services offered to the users, makes it difficult to reuse or exploit the potential of those new resources in multiple scientific fields. In this paper, we present a spatial extract-transform-load (spatial-ETL) approach for downloading atmospheric datasets in order to produce new biometeorological indices and expose them publicly for reuse in research studies. The technologies and processes involved in our work are clearly defined in a context where the GDAL library and the Python programming language are key elements for the development and implementation of the geoprocessing tools. Since the National Oceanic and Atmospheric Administration (NOAA) is the source of information, the ETL process is executed each time this service publishes an updated atmospheric prediction model, thus obtaining different forecasts for spatial and temporal analyses. As a result, we present a web application intended for downloading these newly created datasets after processing, and visualising interactive web maps with the outcomes resulting from a number of geoprocessing tasks. We also elaborate on all functions and technologies used for the design of those processes, with emphasis on the optimisation of the resources as implemented in cloud services.
This paper presents the issue of geographic data storage in NoSQL databases. The authors present the performance investigation of the non-relational database MongoDB with its built-in spatial functions in relation to the PostgreSQL database with a PostGIS spatial extension. As part of the tests, the authors were designed queries simulating common problems in the processing of point data. In addition, the main advantages and disadvantages of NoSQL databases are presented in the context of the ability to manipulate spatial data.
