Aerial and Satellite Imagery and Big Data: Blending Old Technologies with New Trends

Loor, J. Salazar; Fdez-Arróyabe, Pablo

doi:10.1007/978-3-319-89923-7_2

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…At least, in that context, there is a movements (outflow-incoming) of materials and services are guaranteed for freedom (Setiawan, 2016). Big data can include climate information, satellite imagery, digital pictures and videos, transition records or GPS signals, and personal data: a name, a photo, an email address, bank details, posts on social networking websites, medical information, or a computer IP address (Loor & Fdez-Arroyabe, 2018) & (Wu et al, 2016). Such data challenge existing methods due to size, complexity, or rate of availability and requires advanced digital techniques, such as ML models to analyse them.…”

Section: Resultsmentioning

confidence: 99%

Reading Big Data by Machine Learning: The Used of Computer Science for Human Life

Subagio,

Sitepu

2023

jppipa, pendidikan ipa, fisika, biologi, kimia

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Machine learning (ML) models use big data to learn and improve predictability and performance automatically through experience and data, without being programmed to do so by humans. Artificial Intelligence (AI) techniques are being increasingly deployed in finance, in areas such as asset management, algorithmic trading, credit underwriting or blockchain-based finance, enabled by the abundance of available data and by affordable computing capacity. The purpose of this study is to describe in detail how the power of artificial intelligence with its complex system can help the needs of digital technology in the banking sector. The research method used is the elaboration of great thoughts and facts about artificial intelligence. Scientific data is interpreted with analytical power that is as precise as possible, so as to produce a description that meets the logic of structured thinking. The data is taken from relevant and up-to-date literature, the work of scientists who have been disseminated in various weighty scientific publications at the world level. The report can help policy makers to assess the implications of these new technologies and to identify the benefits and risks related to their use. It suggests policy responses that that are intended to support AI innovation in finance while ensuring that its use is consistent with promoting financial stability, market integrity and competition, while protecting financial consumers. Emerging risks from the deployment of AI techniques need to be identified and mitigated to support and promote the use of responsible AI. Existing regulatory and supervisory requirements may need to be clarified and sometimes adjusted, as appropriate, to address some of the perceived incompatibilities of existing arrangements with AI applications.

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Section: Resultsmentioning

confidence: 99%

Reading Big Data by Machine Learning: The Used of Computer Science for Human Life

Subagio,

Sitepu

2023

jppipa, pendidikan ipa, fisika, biologi, kimia

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“…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 [7]. This data is transmitted in real-time to a central server for analysis and integration with other data sources, such as satellite imagery and meteorological data [8]. The use of sensors provides a cost-effective and efficient solution compared to traditional manual monitoring methods [9].…”

Section: Burundimentioning

confidence: 99%

Sensor-Based River Monitoring System: A Case for Kikuletwa River Floods in Tanzania

Mdegela¹,

Bock²,

Municio³

et al. 2023

Preprint

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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.

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“…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].…”

Section: Background and State Of The Artmentioning

confidence: 99%

A Multi-Modal Wireless Sensor System for River Monitoring: A Case for Kikuletwa River Floods in Tanzania

Mdegela

Bock

Municio

et al. 2023

Sensors

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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.

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Aerial and Satellite Imagery and Big Data: Blending Old Technologies with New Trends

Cited by 9 publications

References 25 publications

Reading Big Data by Machine Learning: The Used of Computer Science for Human Life

Reading Big Data by Machine Learning: The Used of Computer Science for Human Life

Sensor-Based River Monitoring System: A Case for Kikuletwa River Floods in Tanzania

A Multi-Modal Wireless Sensor System for River Monitoring: A Case for Kikuletwa River Floods in Tanzania

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