<p>Satellite remote sensing allows for large scale monitoring with low cost and hig... more <p>Satellite remote sensing allows for large scale monitoring with low cost and high revisit time. However, in some applications, it does not provide all the information needed by the analyst for the full characterization of the problem due to, as an example, insufficient resolution or lack of specific measurements. This can lead to inaccuracies in classification and/or detection tasks. Nevertheless, satellite data can be used to guide subsequent discovery, recognition and characterization actions, defining potential areas of interest of limited extension that can be furtherly investigated with on-site strategies. This work presents an innovative framework showing how to use incomplete and inaccurate information extracted by satellite images in order to address on-ground discovery actions aimed to the mapping and the characterization of illegal micro-dumps in Campania Region (Italy). In particular, high-resolution images up to 50 cm resolution are exploited to detect potential micro-dumps by means of a statistical learning method based on spatial features. The detection map is then used to create a priority map based on environmental risk considerations, such as the extension of the area interested by the dump and its proximity to urban settlements, and previous risk mitigation actions. This information is ingested by a planning system in order to allocate and calculate patrolling routes based on the available manpower and vehicles for on-site surveying. The survey is implemented by means of drones equipped with payloads and software allowing for real-time three-dimensional reconstruction of the scene and volumetric estimations. This provides further data to assess the real dangerousness of the site giving to decision makers essential information to plan remediation actions. The system is demonstrated through a case study showing all the stages of the decision process.</p>
Optimization of agricultural practices is key for facing the challenges of modern agri-food syste... more Optimization of agricultural practices is key for facing the challenges of modern agri-food systems, which are expected to satisfy a growing demand of food production in a landscape characterized by a reduction in cultivable lands and an increasing awareness of sustainability issues. In this work, an operational methodology for characterization of vegetation biomass and nitrogen content based on close-range hyperspectral remote sensing is introduced. It is based on an unsupervised active learning technique suitable for the calibration of a partial least square regression. The proposed technique relies on an innovative usage of Shannon’s entropy and allows for the set-up of an incremental monitoring framework from scratch aiming at minimizing field sampling activities. Experimental results concerning the estimation of grassland biomass and nitrogen content returned RMSE values of 2.05 t/ha and 4.68 kg/ha, respectively. They are comparable with the literature, mostly relying on superv...
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
This paper deals with ground displacement measurements with Differential Synthetic Aperture Radar... more This paper deals with ground displacement measurements with Differential Synthetic Aperture Radar Interferometry (DInSAR) technique. These ground modifications often occur as a consequence of an earthquake. The island of Ischia (Southern Italy) has been chosen as case study since it was hit by a severe earthquake on the 21st of August 2017. The National Institute of Geophysics and Volcanology (INGV) and the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council of Italy (CNR) provided the displacement maps considering interferometric pairs close to the main shock event. In this work, a further interferometric pair, which also includes some aftershocks, has been used to calculate ground modifications. The results confirmed a ground subsidence up to 4 centimeters in the epicenter area, in agreement with the reference data. This study rely on open access data as well as on open software, both provided by the European Space Agency (ESA) under the Copernicus program. In particular, data from Sentinel-1 radar satellite mission and SNAP software have been used. The availability of such data and software is relevant for public institutions that can produce valuable information at almost no charge.
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
A flood is a relatively high flow of water that overtops the natural and artificial banks in any ... more A flood is a relatively high flow of water that overtops the natural and artificial banks in any of the reaches of a stream. When banks are overtopped, water spreads over flood plain and generally causes problem for inhabitants, crops and vegetation. During extreme flood events, it is important to determine quickly the extent of flooding and land use under water. Flood maps can be applied to develop comprehensive relief effort immediately after flooding. There are varieties of issues and uncertainties involved in flood mapping. Remotely sensed data can be used to develop flood map in an efficient and effective way. In this paper different techniques of flood mapping using active and passive remote sensing systems are applied, so that a final flood inundation map has been prepared by combining all data within a Geographical Information System (GIS) environment. The flood inundation maps can be further used for quick identification of areas of potential flood hazard to minimize the fl...
<p>In some environmental applications, satellite acquisitions could not be ... more <p>In some environmental applications, satellite acquisitions could not be able to provide all the information necessary to characterize the problem at hand due, for example, to limited spatial resolution or inadequate revisit time. However, in these cases, they can be used for preliminary investigation of the area of interest with the purpose to guide subsequent acquisitions with higher spatial resolution made by means of aerial sensing. This work presents an innovative application combining both satellite acquisitions and aerial close-range sensing implemented via drones in autonomous and coordinated flight. The case study concerns the discovery of illegal micro-dumps and other environmental hazards in Campania Region (Italy). The envisioned workflow includes the detection of target environmental criticalities in very high-resolution optical satellite images and a methodology to plan and adaptively re-plan a survey mission of a team of drones aimed at confirming the presence of a micro-dump and at its characterization. The processing of satellite images is validated on real data in a significative application context, while the performance of the acquisition strategy performed by the drone team are characterized trough simulations on a pre-analysed geographical area.</p>
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
Unmanned Aerial Vehicles (UAVs) are often employed to collect high resolution images in order to ... more Unmanned Aerial Vehicles (UAVs) are often employed to collect high resolution images in order to perform image mosaicking and/or 3D reconstruction. Images are usually stored on board and then processed with on-ground desktop software. In such a way the computational load, and hence the power consumption, is moved on ground, leaving on board only the task of storing data. Such an approach is important in the case of small multi-rotorcraft UAVs because of their low endurance due to the short battery life. Images can be stored on board with either still image or video data compression. Still image system are preferred when low frame rates are involved, because video coding systems are based on motion estimation and compensation algorithms which fail when the motion vectors are significantly long and when the overlapping between subsequent frames is very small. In this scenario, UAVs attitude and position metadata from the Inertial Navigation System (INS) can be employed to estimate glo...
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
2018 IEEE International Conference on Environmental Engineering (EE)
In this paper a set of spectral indices, some of them novel, is suggested in order to effectively... more In this paper a set of spectral indices, some of them novel, is suggested in order to effectively process multispec-tral images, from the NASA and ESA missions Landsat-8 and Sentinel-2, for fire monitoring. The processing aims at extract common products, useful to detect active fires and evaluate the position and extension of the burned areas. The use of open data in comparison with other paid solutions is to remark because of great interest to public and private end users. The thresholding of the proposed indices produces satisfactory results in terms of classification accuracy. Moreover, the joint use of images from Landsat-8 and Sentinel-2, by increasing the overall availability of data, allows also to overcome some limitations on time delivery and high frequency observation, requirements that instead are goodly matched through the use of lower resolution multispectral images such as those from MODIS and VIIRS satellites. The proposed indices have been successfully tested on Vesuvius, that was devastated by terrible fires on the of 11th July 2017. Respect to other works of the same authors on this topic, the performance of the suggested indices are here more deeply investigated, comparing them to alternative products.
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
This paper presents an unsupervised workflow to assess the extension of the burned areas due to w... more This paper presents an unsupervised workflow to assess the extension of the burned areas due to wildfire events over green vegetated areas, in particular woods and forests, based on open access and high spatial resolution multispectral images from the Landsat-8 and Sentinel-2 satellites, together with open access high temporal resolution products (MODIS and VIIRS). The idea is to use jointly these kinds of data to combine the advantages linked to the use of high spatial resolution data with those ones derived from the exploitation of high temporal resolution data. Experimental results, carried on different case studies, show the effectiveness of the proposed method on forest fires, by reaching a performance in terms of the F-measure parameter, that ranges from 0.69 to 0.95, depending on the specific case study.
International Journal of Sustainable Development and Planning, 2016
Enforcement of environmental regulation is a persistent challenge and timely detection of the vio... more Enforcement of environmental regulation is a persistent challenge and timely detection of the violations is key to holding the violators accountable. The use of remote sensing data is becoming an effective practice in the fight against environmental crimes. In this work, a novel and effective approach for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multispectral data together with geospatial analyses in the geographic information system (GIS) environment, is proposed. Experiments on data available for the area of Caserta (Southern Italy), show that the proposed technique provides very high detection capability, up to 90%, with a acceptable false alarm rate, becoming a useful tool in the hand of agencies engaged in the protection of territory.
IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004
We deal with the supervised segmentation of multi-temporal remote-sensing images following a stat... more We deal with the supervised segmentation of multi-temporal remote-sensing images following a statistical Bayesian approach. To take into account prior information on the class of images, like the correlation between neighboring pixels, as well as the available knowledge about the structure of the current image, we model the image as a tree-structured Markov random field. The data collected at two
WIT Transactions on Ecology and The Environment, 2013
IDES-Intelligent Data Extraction System-is the name of a new project, founded by Campania Regiona... more IDES-Intelligent Data Extraction System-is the name of a new project, founded by Campania Regional Government (Italy) and developed by the CIRA-Italian Aerospace Research Centre that aims at implementing an advanced tool to support Government Bodies in the discovery and localization of environmental criticalities. In detail, this project will develop a software and hardware platform for image, data and document analysis in order to support law enforcement investigations. The fight against illegal waste dumping is the first objective of the IDES project and the main goal is to develop algorithms for image analysis in order to automatically identify this environmental criticality. The first task is to identify the statistical link between specific features extracted from satellite images or acquired by airborne sensors, possibly in combination with punctual information (results of chemical analysis, physical property measurements, information pertaining to the population), and environmental criticalities (e.g. illegal dumping, illegal landfills etc). IDES will offer an integrated Geographic Information System (GIS) repository of information extracted and collected by various government bodies. Furthermore, an innovative aspect of IDES is the integration between information extracted from remotely sensed images and information extracted from textual documents through a semantic analysis. The results of this last analysis will be a taxonomydriven document searching and browsing system in which all documents are categorized and all relevant entities in documents (e.g. people, organizations, places and events) are extracted and represented by means of concept maps. Finally, these results: a) provide support to government bodies "end-users"; b) can be included in a GIS; c) can constitute the basis for the application of Artificial Adaptive Systems used in the domain of semantic analysis of
This study presents the map of the external effective annual dose rate (1:200,000 scale) due to t... more This study presents the map of the external effective annual dose rate (1:200,000 scale) due to terrestrial and cosmic radiation. The terrestrial dose is assessed via gamma ray spectroscopy combining radiometric data from airborne surveys and laboratory measurements. The geostatistical method Collocated CoKriging is used for the spatial interpolation of the sparse gamma ray data, adopting a high-resolution geological map as ancillary information. The obtained numerical map is integrated with the cosmic radiation effective dose rate calculated using the CARI-7 software tool that considers the effects of altitude, latitude, and the solar magnetic activity cycle. The absorbed dose rate due to radioactivity of the main lithological groups is studied and, for the most populated municipalities, the populationweighted average effective dose is also calculated. For future generations, this map will be a reference tool for evaluating radiological effects in case of accidental events like radioactive fallout or environmental contaminations.
Soil texture is key information in agriculture for improving soil knowledge and crop performance,... more Soil texture is key information in agriculture for improving soil knowledge and crop performance, so the accurate mapping of this crucial feature is imperative for rationally planning cultivations and for targeting interventions. We studied the relationship between radioelements and soil texture in the Mezzano Lowland (Italy), a 189 km2 agricultural plain investigated through a dedicated airborne gamma-ray spectroscopy survey. The K and Th abundances were used to retrieve the clay and sand content by means of a multi-approach method. Linear (simple and multiple) and non-linear (machine learning algorithms with deep neural networks) predictive models were trained and tested adopting a 1:50,000 scale soil texture map. The comparison of these approaches highlighted that the non-linear model introduces significant improvements in the prediction of soil texture fractions. The predicted maps of the clay and of the sand content were compared with the regional soil maps. Although the macro-...
2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )
This paper describes a framework to develop cost-benefit analyses of the use of predictive mainte... more This paper describes a framework to develop cost-benefit analyses of the use of predictive maintenance technologies, i.e., sensors and network infrastructure, in order to optimise field maintenance activities in situations where there is a need for maintenance personnel to move across a wide geographical area. The proposed methodologies can be used both in the strategic phase, in which it is necessary to make the choice of the maintenance strategy, and in the operational phase, in which, through decision support systems, it is necessary to plan maintenance interventions in the current maintenance cycle. In order to illustrate the proposed methodologies, a toy problem is defined and developed via Monte Carlo simulations. Experiments show the effectiveness of the proposed framework.
<p>Vertical take-off and landing Unmanned Aerial Vehicles (UAVs) for Gamma-... more <p>Vertical take-off and landing Unmanned Aerial Vehicles (UAVs) for Gamma-Ray Surveys (GRS) provide a cost-effective and timely approach tool for environmental radioactivity mapping. The UAV technique combines the advantages of ground and airborne measurements:  there is no need for an airport for take-off and landing, and high spatial resolution surveys can also be performed in dangerous areas without endangering the operators.</p><p>The main limitation of existing UAVs for GRS is the lack of software and hardware integration between avionics systems and radiation detectors. RadHawk fills this gap with an advanced mechanical, electronic, and software connection between a specifically developed quadcopter and a digital Multi-Channel Analyzer GammaStream (GS). The GS is coupled with a 2” CeBr<sub>3</sub> scintillator having spectral energy resolution ~60% better than that of a NaI for <sup>137</sup>C detection. Communication between the GS onboard microcomputer and the drone’s autopilot Pixhawk is achieved through a custom protocol which allows sharing telemetry updates and executing commands.</p><p>The best spatial resolution of radiometric data is achieved through a list mode real-time processing that generates, with optimized acquisition time, energy calibrated georeferenced gamma spectra. A radio frequency transceiver module sends data to a control station, where the user can easily control the flight path and check the artificial radionuclides warning for real-time identifying of hotspots.</p><p>A post-processing algorithm based on a Full Spectrum Analysis – Maximum Likelihood Estimation was developed to enhance the identification capability of anthropogenic radionuclides and to produce maps of the K, Th and U abundances of the investigated areas.</p>
<p>Soil texture is a key information in precision agriculture for improving... more <p>Soil texture is a key information in precision agriculture for improving soil knowledge and crop performances. A precise mapping of its variability is thereby imperative for rationally planning cultivations and targeting interventions. Unlike direct soil texture measurements that are punctual, destructive, and time-consuming, remote sensing surveys can give widespread, non-invasive, and fast indirect evidence of clay, silt, and sand content. In this study we investigate the performance of Airborne Gamma Ray Spectroscopy (AGRS) for discriminating different texture classes in the ternary diagram of soil texture.</p><p>The Mezzano valley (Ferrara, Italy), a 180 km<sup>2</sup> rural area reclaimed in the last century, represents an extraordinary benchmark for validating our method. This area, for which a public soil texture map at 1:50000 scale and a spatial resolution of 500 m is available, was scanned by an AGRS system mounted on a dedicated aircraft. The aircraft flew over the study area in a grid-like path of ~500 m spacing, collecting 1469 geolocalized spectra. The K and Th punctual measurements were spatially interpolated by Ordinary Kriging to elaborate K and Th maps with the identical spatial resolution of the soil texture map. Simple and multiple linear correlations, as well as a non‑linear Machine Learning algorithm, were then performed between gamma and soil texture data.</p><p>The obtained results by a simple linear regression analysis highlight a moderate positive (negative) correlation between clay (sand) content and K and Th abundances. Multiple linear regressions show a similar trend, with the limitation that the calculated clay, silt, and sand values populate the soil texture ternary diagram in a straight line. Finally, we demonstrate that the most accurate reconstruction of soil texture values is obtained by a non-linear fitting based on the Machine Learning algorithm.</p>
<p>Satellite remote sensing allows for large scale monitoring with low cost and hig... more <p>Satellite remote sensing allows for large scale monitoring with low cost and high revisit time. However, in some applications, it does not provide all the information needed by the analyst for the full characterization of the problem due to, as an example, insufficient resolution or lack of specific measurements. This can lead to inaccuracies in classification and/or detection tasks. Nevertheless, satellite data can be used to guide subsequent discovery, recognition and characterization actions, defining potential areas of interest of limited extension that can be furtherly investigated with on-site strategies. This work presents an innovative framework showing how to use incomplete and inaccurate information extracted by satellite images in order to address on-ground discovery actions aimed to the mapping and the characterization of illegal micro-dumps in Campania Region (Italy). In particular, high-resolution images up to 50 cm resolution are exploited to detect potential micro-dumps by means of a statistical learning method based on spatial features. The detection map is then used to create a priority map based on environmental risk considerations, such as the extension of the area interested by the dump and its proximity to urban settlements, and previous risk mitigation actions. This information is ingested by a planning system in order to allocate and calculate patrolling routes based on the available manpower and vehicles for on-site surveying. The survey is implemented by means of drones equipped with payloads and software allowing for real-time three-dimensional reconstruction of the scene and volumetric estimations. This provides further data to assess the real dangerousness of the site giving to decision makers essential information to plan remediation actions. The system is demonstrated through a case study showing all the stages of the decision process.</p>
Optimization of agricultural practices is key for facing the challenges of modern agri-food syste... more Optimization of agricultural practices is key for facing the challenges of modern agri-food systems, which are expected to satisfy a growing demand of food production in a landscape characterized by a reduction in cultivable lands and an increasing awareness of sustainability issues. In this work, an operational methodology for characterization of vegetation biomass and nitrogen content based on close-range hyperspectral remote sensing is introduced. It is based on an unsupervised active learning technique suitable for the calibration of a partial least square regression. The proposed technique relies on an innovative usage of Shannon’s entropy and allows for the set-up of an incremental monitoring framework from scratch aiming at minimizing field sampling activities. Experimental results concerning the estimation of grassland biomass and nitrogen content returned RMSE values of 2.05 t/ha and 4.68 kg/ha, respectively. They are comparable with the literature, mostly relying on superv...
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
This paper deals with ground displacement measurements with Differential Synthetic Aperture Radar... more This paper deals with ground displacement measurements with Differential Synthetic Aperture Radar Interferometry (DInSAR) technique. These ground modifications often occur as a consequence of an earthquake. The island of Ischia (Southern Italy) has been chosen as case study since it was hit by a severe earthquake on the 21st of August 2017. The National Institute of Geophysics and Volcanology (INGV) and the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council of Italy (CNR) provided the displacement maps considering interferometric pairs close to the main shock event. In this work, a further interferometric pair, which also includes some aftershocks, has been used to calculate ground modifications. The results confirmed a ground subsidence up to 4 centimeters in the epicenter area, in agreement with the reference data. This study rely on open access data as well as on open software, both provided by the European Space Agency (ESA) under the Copernicus program. In particular, data from Sentinel-1 radar satellite mission and SNAP software have been used. The availability of such data and software is relevant for public institutions that can produce valuable information at almost no charge.
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
A flood is a relatively high flow of water that overtops the natural and artificial banks in any ... more A flood is a relatively high flow of water that overtops the natural and artificial banks in any of the reaches of a stream. When banks are overtopped, water spreads over flood plain and generally causes problem for inhabitants, crops and vegetation. During extreme flood events, it is important to determine quickly the extent of flooding and land use under water. Flood maps can be applied to develop comprehensive relief effort immediately after flooding. There are varieties of issues and uncertainties involved in flood mapping. Remotely sensed data can be used to develop flood map in an efficient and effective way. In this paper different techniques of flood mapping using active and passive remote sensing systems are applied, so that a final flood inundation map has been prepared by combining all data within a Geographical Information System (GIS) environment. The flood inundation maps can be further used for quick identification of areas of potential flood hazard to minimize the fl...
<p>In some environmental applications, satellite acquisitions could not be ... more <p>In some environmental applications, satellite acquisitions could not be able to provide all the information necessary to characterize the problem at hand due, for example, to limited spatial resolution or inadequate revisit time. However, in these cases, they can be used for preliminary investigation of the area of interest with the purpose to guide subsequent acquisitions with higher spatial resolution made by means of aerial sensing. This work presents an innovative application combining both satellite acquisitions and aerial close-range sensing implemented via drones in autonomous and coordinated flight. The case study concerns the discovery of illegal micro-dumps and other environmental hazards in Campania Region (Italy). The envisioned workflow includes the detection of target environmental criticalities in very high-resolution optical satellite images and a methodology to plan and adaptively re-plan a survey mission of a team of drones aimed at confirming the presence of a micro-dump and at its characterization. The processing of satellite images is validated on real data in a significative application context, while the performance of the acquisition strategy performed by the drone team are characterized trough simulations on a pre-analysed geographical area.</p>
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
Unmanned Aerial Vehicles (UAVs) are often employed to collect high resolution images in order to ... more Unmanned Aerial Vehicles (UAVs) are often employed to collect high resolution images in order to perform image mosaicking and/or 3D reconstruction. Images are usually stored on board and then processed with on-ground desktop software. In such a way the computational load, and hence the power consumption, is moved on ground, leaving on board only the task of storing data. Such an approach is important in the case of small multi-rotorcraft UAVs because of their low endurance due to the short battery life. Images can be stored on board with either still image or video data compression. Still image system are preferred when low frame rates are involved, because video coding systems are based on motion estimation and compensation algorithms which fail when the motion vectors are significantly long and when the overlapping between subsequent frames is very small. In this scenario, UAVs attitude and position metadata from the Inertial Navigation System (INS) can be employed to estimate glo...
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
2018 IEEE International Conference on Environmental Engineering (EE)
In this paper a set of spectral indices, some of them novel, is suggested in order to effectively... more In this paper a set of spectral indices, some of them novel, is suggested in order to effectively process multispec-tral images, from the NASA and ESA missions Landsat-8 and Sentinel-2, for fire monitoring. The processing aims at extract common products, useful to detect active fires and evaluate the position and extension of the burned areas. The use of open data in comparison with other paid solutions is to remark because of great interest to public and private end users. The thresholding of the proposed indices produces satisfactory results in terms of classification accuracy. Moreover, the joint use of images from Landsat-8 and Sentinel-2, by increasing the overall availability of data, allows also to overcome some limitations on time delivery and high frequency observation, requirements that instead are goodly matched through the use of lower resolution multispectral images such as those from MODIS and VIIRS satellites. The proposed indices have been successfully tested on Vesuvius, that was devastated by terrible fires on the of 11th July 2017. Respect to other works of the same authors on this topic, the performance of the suggested indices are here more deeply investigated, comparing them to alternative products.
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
This paper presents an unsupervised workflow to assess the extension of the burned areas due to w... more This paper presents an unsupervised workflow to assess the extension of the burned areas due to wildfire events over green vegetated areas, in particular woods and forests, based on open access and high spatial resolution multispectral images from the Landsat-8 and Sentinel-2 satellites, together with open access high temporal resolution products (MODIS and VIIRS). The idea is to use jointly these kinds of data to combine the advantages linked to the use of high spatial resolution data with those ones derived from the exploitation of high temporal resolution data. Experimental results, carried on different case studies, show the effectiveness of the proposed method on forest fires, by reaching a performance in terms of the F-measure parameter, that ranges from 0.69 to 0.95, depending on the specific case study.
International Journal of Sustainable Development and Planning, 2016
Enforcement of environmental regulation is a persistent challenge and timely detection of the vio... more Enforcement of environmental regulation is a persistent challenge and timely detection of the violations is key to holding the violators accountable. The use of remote sensing data is becoming an effective practice in the fight against environmental crimes. In this work, a novel and effective approach for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multispectral data together with geospatial analyses in the geographic information system (GIS) environment, is proposed. Experiments on data available for the area of Caserta (Southern Italy), show that the proposed technique provides very high detection capability, up to 90%, with a acceptable false alarm rate, becoming a useful tool in the hand of agencies engaged in the protection of territory.
IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004
We deal with the supervised segmentation of multi-temporal remote-sensing images following a stat... more We deal with the supervised segmentation of multi-temporal remote-sensing images following a statistical Bayesian approach. To take into account prior information on the class of images, like the correlation between neighboring pixels, as well as the available knowledge about the structure of the current image, we model the image as a tree-structured Markov random field. The data collected at two
WIT Transactions on Ecology and The Environment, 2013
IDES-Intelligent Data Extraction System-is the name of a new project, founded by Campania Regiona... more IDES-Intelligent Data Extraction System-is the name of a new project, founded by Campania Regional Government (Italy) and developed by the CIRA-Italian Aerospace Research Centre that aims at implementing an advanced tool to support Government Bodies in the discovery and localization of environmental criticalities. In detail, this project will develop a software and hardware platform for image, data and document analysis in order to support law enforcement investigations. The fight against illegal waste dumping is the first objective of the IDES project and the main goal is to develop algorithms for image analysis in order to automatically identify this environmental criticality. The first task is to identify the statistical link between specific features extracted from satellite images or acquired by airborne sensors, possibly in combination with punctual information (results of chemical analysis, physical property measurements, information pertaining to the population), and environmental criticalities (e.g. illegal dumping, illegal landfills etc). IDES will offer an integrated Geographic Information System (GIS) repository of information extracted and collected by various government bodies. Furthermore, an innovative aspect of IDES is the integration between information extracted from remotely sensed images and information extracted from textual documents through a semantic analysis. The results of this last analysis will be a taxonomydriven document searching and browsing system in which all documents are categorized and all relevant entities in documents (e.g. people, organizations, places and events) are extracted and represented by means of concept maps. Finally, these results: a) provide support to government bodies "end-users"; b) can be included in a GIS; c) can constitute the basis for the application of Artificial Adaptive Systems used in the domain of semantic analysis of
This study presents the map of the external effective annual dose rate (1:200,000 scale) due to t... more This study presents the map of the external effective annual dose rate (1:200,000 scale) due to terrestrial and cosmic radiation. The terrestrial dose is assessed via gamma ray spectroscopy combining radiometric data from airborne surveys and laboratory measurements. The geostatistical method Collocated CoKriging is used for the spatial interpolation of the sparse gamma ray data, adopting a high-resolution geological map as ancillary information. The obtained numerical map is integrated with the cosmic radiation effective dose rate calculated using the CARI-7 software tool that considers the effects of altitude, latitude, and the solar magnetic activity cycle. The absorbed dose rate due to radioactivity of the main lithological groups is studied and, for the most populated municipalities, the populationweighted average effective dose is also calculated. For future generations, this map will be a reference tool for evaluating radiological effects in case of accidental events like radioactive fallout or environmental contaminations.
Soil texture is key information in agriculture for improving soil knowledge and crop performance,... more Soil texture is key information in agriculture for improving soil knowledge and crop performance, so the accurate mapping of this crucial feature is imperative for rationally planning cultivations and for targeting interventions. We studied the relationship between radioelements and soil texture in the Mezzano Lowland (Italy), a 189 km2 agricultural plain investigated through a dedicated airborne gamma-ray spectroscopy survey. The K and Th abundances were used to retrieve the clay and sand content by means of a multi-approach method. Linear (simple and multiple) and non-linear (machine learning algorithms with deep neural networks) predictive models were trained and tested adopting a 1:50,000 scale soil texture map. The comparison of these approaches highlighted that the non-linear model introduces significant improvements in the prediction of soil texture fractions. The predicted maps of the clay and of the sand content were compared with the regional soil maps. Although the macro-...
2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )
This paper describes a framework to develop cost-benefit analyses of the use of predictive mainte... more This paper describes a framework to develop cost-benefit analyses of the use of predictive maintenance technologies, i.e., sensors and network infrastructure, in order to optimise field maintenance activities in situations where there is a need for maintenance personnel to move across a wide geographical area. The proposed methodologies can be used both in the strategic phase, in which it is necessary to make the choice of the maintenance strategy, and in the operational phase, in which, through decision support systems, it is necessary to plan maintenance interventions in the current maintenance cycle. In order to illustrate the proposed methodologies, a toy problem is defined and developed via Monte Carlo simulations. Experiments show the effectiveness of the proposed framework.
<p>Vertical take-off and landing Unmanned Aerial Vehicles (UAVs) for Gamma-... more <p>Vertical take-off and landing Unmanned Aerial Vehicles (UAVs) for Gamma-Ray Surveys (GRS) provide a cost-effective and timely approach tool for environmental radioactivity mapping. The UAV technique combines the advantages of ground and airborne measurements:  there is no need for an airport for take-off and landing, and high spatial resolution surveys can also be performed in dangerous areas without endangering the operators.</p><p>The main limitation of existing UAVs for GRS is the lack of software and hardware integration between avionics systems and radiation detectors. RadHawk fills this gap with an advanced mechanical, electronic, and software connection between a specifically developed quadcopter and a digital Multi-Channel Analyzer GammaStream (GS). The GS is coupled with a 2” CeBr<sub>3</sub> scintillator having spectral energy resolution ~60% better than that of a NaI for <sup>137</sup>C detection. Communication between the GS onboard microcomputer and the drone’s autopilot Pixhawk is achieved through a custom protocol which allows sharing telemetry updates and executing commands.</p><p>The best spatial resolution of radiometric data is achieved through a list mode real-time processing that generates, with optimized acquisition time, energy calibrated georeferenced gamma spectra. A radio frequency transceiver module sends data to a control station, where the user can easily control the flight path and check the artificial radionuclides warning for real-time identifying of hotspots.</p><p>A post-processing algorithm based on a Full Spectrum Analysis – Maximum Likelihood Estimation was developed to enhance the identification capability of anthropogenic radionuclides and to produce maps of the K, Th and U abundances of the investigated areas.</p>
<p>Soil texture is a key information in precision agriculture for improving... more <p>Soil texture is a key information in precision agriculture for improving soil knowledge and crop performances. A precise mapping of its variability is thereby imperative for rationally planning cultivations and targeting interventions. Unlike direct soil texture measurements that are punctual, destructive, and time-consuming, remote sensing surveys can give widespread, non-invasive, and fast indirect evidence of clay, silt, and sand content. In this study we investigate the performance of Airborne Gamma Ray Spectroscopy (AGRS) for discriminating different texture classes in the ternary diagram of soil texture.</p><p>The Mezzano valley (Ferrara, Italy), a 180 km<sup>2</sup> rural area reclaimed in the last century, represents an extraordinary benchmark for validating our method. This area, for which a public soil texture map at 1:50000 scale and a spatial resolution of 500 m is available, was scanned by an AGRS system mounted on a dedicated aircraft. The aircraft flew over the study area in a grid-like path of ~500 m spacing, collecting 1469 geolocalized spectra. The K and Th punctual measurements were spatially interpolated by Ordinary Kriging to elaborate K and Th maps with the identical spatial resolution of the soil texture map. Simple and multiple linear correlations, as well as a non‑linear Machine Learning algorithm, were then performed between gamma and soil texture data.</p><p>The obtained results by a simple linear regression analysis highlight a moderate positive (negative) correlation between clay (sand) content and K and Th abundances. Multiple linear regressions show a similar trend, with the limitation that the calculated clay, silt, and sand values populate the soil texture ternary diagram in a straight line. Finally, we demonstrate that the most accurate reconstruction of soil texture values is obtained by a non-linear fitting based on the Machine Learning algorithm.</p>
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Papers by Luca Cicala