RPAS and GIS for landfill analysis

Daugėla, Ignas; Visockienė, Jūratė Sužiedelytė; Aksamitauskas, Vladislovas Česlovas

doi:10.1051/e3sconf/20184400025

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…RPAS contain several sub-systems that comprise three key components: an RPA, a GCS, and the communication system [12], [19], as seen in Figure 1. Autonomous Control is achieved when an RPA is equipped with onboard computers [20] that allow RPAS to control navigation and decisionmaking processes under significant uncertainty with minimal or no human interference [21].…”

Section: Motivation For Concept Of Autonomous Controlmentioning

confidence: 99%

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Survey of RPAS Autonomous Control Systems Using Artificial Intelligence

Aibin¹,

Aldiab

Bhavsar³

et al. 2021

IEEE Access

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In this survey, we look at the overall idea of Remotely Piloted Aircraft Systems (RPAS) and autonomous control, as well as RPAS infrastructure, levels of autonomy, and current benefits and difficulties in the field when utilizing Artificial Intelligence. While current remotely piloted aircraft systems have a manual pilot operator to provide double-layer security and safety, studies show that having RPAS with a fully autonomous vehicle at its centre could significantly improve decision-making and overall mission precision, accuracy, safety, and efficiency.

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Section: Motivation For Concept Of Autonomous Controlmentioning

confidence: 99%

“…They are increasingly integrated into the components of Remotely Piloted Aircraft Systems (RPAS), which include the Remotely Piloted Aircraft (RPA), referred to in some literature as drones or Unmanned Aerial Vehicles (UAV); a Ground Control Station (GCS); and a communication data link [12].…”

Section: Introductionmentioning

confidence: 99%

Survey of RPAS Autonomous Control Systems Using Artificial Intelligence

Aibin¹,

Aldiab

Bhavsar³

et al. 2021

IEEE Access

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“…Therefore, additional remote sensing studies have been made by authors in Kariotiskes landfill territory (Fig. 1a) [15].…”

Section: Test Areamentioning

confidence: 99%

Analysis of Air Pollution Parameters Using Covariance Function Theory

Daugėla

Visockienė

Skeivalas

2020

Ecological Chemistry and Engineering S

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The paper analyses the intensity changes of three pollution parameter vectors in space and time. The RGB raster pollution data of the Lithuanian territory used for the research were prepared according to the digital images of the Sentinel-2 Earth satellites. The numerical vectors of environmental pollution parameters CH4 (methane), NO2 (nitrogen dioxide) and for direct comparison O2 (oxygen gas) were used for the calculations. The covariance function theory was used to perform the analysis of intensity changes in digital vectors. Estimates of the covariance functions of the numerical vectors of pollution parameters and O2 or the auto-covariance functions of single vectors are calculated from random functions consisting of arrays of measurement parameters of all parameters vectors. Correlation between parameters vectors depends on the density of parameters and their structure. Estimates of covariance functions were calculated by changing the quantization interval on a time scale and using a compiled computer program using the Matlab procedure package. The probability dependence between the environmental pollution parameter vectors and trace gas of the territory in Lithuania and their change in time scale was determined.

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“…Earth observation, from satellite to unmanned aerial vehicles (UAVs), is a promising technology for monitoring these obligations. Beaumont et al [2] studied the potential of satellite and airborne remote sensing for detecting landfills regionally and characterizing them locally, namely for the detection of biogas leaks, wells and pipes and concluded that UAVs offer perspectives for the topographical monitoring of the complete landfill area, an application that has largely been studied since then [3][4]. Still, the mapping of landfill common LC features such as waste areas, infrastructure, exposed membranes, vegetation that are important for the operational and daily management of the site, has not been properly studied.…”

Section: Introductionmentioning

confidence: 99%

UAVs for Fine-Scale Open-Source Landfill Mapping

Wyard

Beaumont

Grippa

et al. 2021

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

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Landfill managers are subject to obligations which include the regular monitoring of the topographical and land cover (LC) evolution of the site. This research aims at developing a cost-effective non-intrusive methodology for mapping landfill LC features. To this end, a state-of-the-art OBIA open-source workflow based on an integration of GRASS GIS and Python programming environment was adapted and applied to 3-cm optical UAV image acquired over the landfill site of Hallembaye (Belgium). The results of this 8class supervised classification are promising with an overall accuracy of 80.5%. This study shows that existing opensource processing chain can be adapted to UAV imagery. In addition, the added value of feature selection and of textural information provided by very high-resolution optical data is also highlighted. Finally, this study illustrates the potential of machine learning for the monitoring of landfill sites.

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RPAS and GIS for landfill analysis

Cited by 5 publications

References 11 publications

Survey of RPAS Autonomous Control Systems Using Artificial Intelligence

Survey of RPAS Autonomous Control Systems Using Artificial Intelligence

Analysis of Air Pollution Parameters Using Covariance Function Theory

UAVs for Fine-Scale Open-Source Landfill Mapping

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