Development of a Java-based application for environmental remote sensing data processing

Semlali, Badr-Eddine Boudriki; Amrani, Chaker El; Denys, Siegfried

doi:10.11591/ijece.v9i3.pp1978-1986

Cited by 13 publications

(6 citation statements)

References 10 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this reason, air quality (AQ) currently deserves special attention from several scientific communities. Indeed, continuous AQ monitoring is one of the proposed solutions helping to decision-makers [2]. It enables a nearreal-time (NRT) monitoring of the aerosol optical depths (AOD), it provides a potential input data for AQ models, tracks the pollutant plumes emitted from industrial and agricultural areas, [2] and estimates the ozone (O3) precursor.…”

Section: Introductionmentioning

confidence: 99%

Big data and remote sensing: A new software of ingestion

Semlali

Amrani

2021

IJECE

View full text Add to dashboard Cite

Currently, remote sensing is widely used in environmental monitoring applications, mostly air quality mapping and climate change supervision. However, satellite sensors occur massive volumes of data in near-real-time, stored in multiple formats and are provided with high velocity and variety. Besides, the processing of satellite big data is challenging. Thus, this study aims to approve that satellite data are big data and proposes a new big data architecture for satellite data processing. The developed software is enabling an efficient remote sensing big data ingestion and preprocessing. As a result, the experiment results show that 86 percent of the unnecessary daily files are discarded with a data cleansing of 20 percent of the erroneous and inaccurate plots. The final output is integrated into the Hadoop system, especially the HDFS, HBase, and Hive, for extra calculation and processing.

show abstract

Section: Introductionmentioning

confidence: 99%

Big data and remote sensing: A new software of ingestion

Semlali

Amrani

2021

IJECE

View full text Add to dashboard Cite

show abstract

“…Thus, the proposed cloud-based RS production system manages massive RS data, and various products are generated. Some appropriate methods that focus on novel architectures for RS processing are explained as follows: Boudriki Semlali [20] developed Java-based application software to collect, process, and visualize numerous environmental data acquired from the EUMETSAT datacenter. Boudriki Semlali et al [21] also proposed software as an extract-transform-load tool for satellite data pre-processing that allows effective RSBD integration.…”

Section: Related Workmentioning

confidence: 99%

SAT-Hadoop-Processor: A Distributed Remote Sensing Big Data Processing Software for Earth Observation Applications

Semlali

Freitag²

2021

Applied Sciences

View full text Add to dashboard Cite

Nowadays, several environmental applications take advantage of remote sensing techniques. A considerable volume of this remote sensing data occurs in near real-time. Such data are diverse and are provided with high velocity and variety, their pre-processing requires large computing capacities, and a fast execution time is critical. This paper proposes a new distributed software for remote sensing data pre-processing and ingestion using cloud computing technology, specifically OpenStack. The developed software discarded 86% of the unneeded daily files and removed around 20% of the erroneous and inaccurate datasets. The parallel processing optimized the total execution time by 90%. Finally, the software efficiently processed and integrated data into the Hadoop storage system, notably the HDFS, HBase, and Hive.

show abstract

“…Satellite programs such as landsat [9], sentinel, aqua/terra, among others, provide a wealth of freely available data sets for the masses. This tremendous progress has unlocked many innovations that extract valuable insights [10][11] from satellite imagery using big data pipelines and advanced machine learning systems [12].…”

Section: Introductionmentioning

confidence: 99%

Satellite image inpainting with deep generative adversarial neural networks

Zaytar

Amrani

2021

IJ-AI

View full text Add to dashboard Cite

This work addresses the problem of recovering lost or damaged satellite image pixels (gaps) caused by sensor processing errors or by natural phenomena like cloud presence. Such errors decrease our ability to monitor regions of interest and significantly increase the average revisit time for all satellites. This paper presents a novel neural system based on conditional deep generative adversarial networks (cGAN) optimized to fill satellite imagery gaps using surrounding pixel values and static high-resolution visual priors. Experimental results show that the proposed system outperforms traditional and neural network baselines. It achieves a normalized least absolute deviations error of ( & decrease in error compared with the two baselines) and a mean squared error loss of ( & decrease in error) over the test set. The model can be deployed within a remote sensing data pipeline to reconstruct missing pixel measurements for near-real-time monitoring and inference purposes, thus empowering policymakers and users to make environmentally informed decisions.

show abstract

Development of a Java-based application for environmental remote sensing data processing

Cited by 13 publications

References 10 publications

Big data and remote sensing: A new software of ingestion

Big data and remote sensing: A new software of ingestion

SAT-Hadoop-Processor: A Distributed Remote Sensing Big Data Processing Software for Earth Observation Applications

Satellite image inpainting with deep generative adversarial neural networks

Contact Info

Product

Resources

About