Detection and Geometrical Characterization of a Buried Landfill Site by Integrating Land Use Historical Analysis, Digital Photogrammetry and Airborne Lidar Data

Abandoned quarries are frequently used as sites of illegal dumping of solid urban waste. These sites often occur nearby or within urban areas so that their detection may turn out to be quite difficult from the surface. This study focuses on the detection and geometrical characterization of a hidden landfill site located along the coastline of the Campi Flegrei, near Naples, Italy. Our approach is based on the analysis of historical topographic maps and aerial photographs, coupled with quantitative comparison of multitemporal digital elevation models obtained by digital photogrammetry and lidar techniques. The comparative analysis of topographic maps and aerial photos clearly shows modifications of the landscape associated with the urban development and quarrying activity, as well as the later filling of the quarry. The change detection analysis reveals that remarkable elevation changes occurred in the study area between 1956 and 2008. The average thickness of the landfill deposits is ca. 8 m, whereas the average volume is ca. 100,000 m3. The results of this work confirm the suitability of the used methodological approach that combines both qualitative and quantitative techniques for the detection of buried landfill sites. The geometric characterization of a landfill represents a fitting starting point for the further planning of geophysical site surveys and direct investigations aimed at the assessment of environmental hazards.

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Uso do LiDAR para avaliar os padrões hídricos de bacias em áreas urbanas: Caracterização fisiográfica da bacia do Rio Beberibe, PE

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v13.07.p3659-3674 ◽

2020 ◽

Vol 13 (07) ◽

pp. 3659

Author(s):

Gabriel Antonio Silva Soares ◽

Josiclêda Domiciano Galvíncio

Keyword(s):

River Basin ◽

Urban Areas ◽

Three Dimensional ◽

Drainage Density ◽

Sensor Data ◽

Aerial Photographs ◽

Sinuosity Index ◽

Main River ◽

Circularity Index

A caracterização fisiográfica, consiste no levantamento dos principais parâmetros fisiográficos de uma bacia, que podem ser extraídos de mapas, fotografias aéreas e imagens de satélite, e se apresenta como uma ferramenta útil ao planejamento e gestão dos recursos hídricos, por existir uma forte correspondência entre as características físicas de uma bacia hidrográfica e seu regime hidrológico. O presente estudo se propõe a caracterizar fisiograficamente a bacia hidrográfica do Rio Beberibe utilizando os dados do sensor LiDAR com resolução espacial de 5 m em uma escala de 1:5000, provenientes do programa PE3D (Pernambuco Tridimensional). Os resultados obtidos sobre os parâmetros geométricos, levaram à conclusão de que a bacia hidrográfica do Rio Beberibe não é naturalmente propensa à ocorrência de enchentes, por possuir uma forma alongada. Sobre os padrões de drenagem, foi possível concluir que a bacia possui uma drenagem rica e escoamento superficial fluido e ágil, que seus canais possuem perfis retilíneos, e que se trata de uma bacia de 6ª ordem. Acerca das características do relevo, foi constatado que a bacia não possui picos de altitudes elevados, porém uma considerável amplitude altimétrica, além de que na declividade da bacia não estão apresentadas inclinações bruscas. Using LiDAR to evaluate water patterns in urban areas basin: Physiographic characterization of the Rio Beberibe basin, PE A B S T R A C TThe physiographic characterization consists in the survey of the main physiographic parameters of a basin, which can be extracted from maps, aerial photographs and satellite images, and presents itself as a useful tool for planning and management of water resources, as a consequence of the influence between the physical characteristics of a river basin and its hydrological behaviour. The selection of the Beberibe river basin for the development of this research was made due to their importance for the macrodrainage of the state of Pernambuco, and because they are located in an area of strong urban activity. Thus, these areas present strong socioeconomic activity, which configures them as spaces where water planning is crucial for the conscious use of their resources. Faced with this scenario, the present study proposes to physiographically characterize the Beberibe river basin using LiDAR sensor data with a spatial resolution of 5 m on a scale of 1:5000, from the PE3D program (Three-Dimensional Pernambuco). The main morphometric parameters obtained for the Beberibe River basin were grouped into three groups of characteristics. The geometric characteristics, which consist of the following parameters: drainage area (A), basin perimeter (P), axial length (L), compacity coefficient (Kc), shape factor (Kf), and circularity index (IC). The characteristics of the drainage network, which consists in the survey of the following parameters: length of the main river (Lc), total length of the channels (Lt), basin order, drainage density (Dd), hydrographic density (Dh), sinuosity index (Is), and average runoff length (

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Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics

Urban Informatics - The Urban Book Series ◽

10.1007/978-981-15-8983-6_22 ◽

2021 ◽

pp. 367-400

Author(s):

Wei Yao ◽

Jianwei Wu

Keyword(s):

Performance Analysis ◽

Urban Areas ◽

Point Clouds ◽

Airborne Lidar ◽

Training Data ◽

Detection Accuracy ◽

Traffic Dynamics ◽

Positive Rate ◽

Adaboost Classifier

AbstractIn this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR. Both static and dynamical properties of large-scale urban areas can be characterized in a highly automatic way. First, LiDAR point clouds are colorized by co-registration with images if available. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per-pixel or per-point. Then, various spatial-statistical and spectral features can be extracted using a cuboid volumetric neighborhood. The most important features highlighted by the feature-relevance assessment, such as LiDAR intensity, NDVI, and planarity or covariance-based features, are selected to span the feature space for the AdaBoost classifier. Classification results as labeled points or pixels are acquired based on pre-selected training data for the objects of building, tree, vehicle, and natural ground. Based on the urban classification results, traffic-related vehicle motion can further be indicated and determined by analyzing and inverting the motion artifact model pertinent to airborne LiDAR. The performance of the developed strategy towards detecting various urban objects is extensively evaluated using both public ISPRS benchmarks and peculiar experimental datasets, which were acquired across European and Canadian downtown areas. Both semantic and geometric criteria are used to assess the experimental results at both per-pixel and per-object levels. In the datasets of typical city areas requiring co-registration of imagery and LiDAR point clouds a priori, the AdaBoost classifier achieves a detection accuracy of up to 90% for buildings, up to 72% for trees, and up to 80% for natural ground, while a low and robust false-positive rate is observed for all the test sites regardless of object class to be evaluated. Both theoretical and simulated studies for performance analysis show that the velocity estimation of fast-moving vehicles is promising and accurate, whereas slow-moving ones are hard to distinguish and yet estimated with acceptable velocity accuracy. Moreover, the point density of ALS data tends to be related to system performance. The velocity can be estimated with high accuracy for nearly all possible observation geometries except for those vehicles moving in or (quasi-)along the track. By comparative performance analysis of the test sites, the performance and consistent reliability of the developed strategy for the detection and characterization of urban objects and traffic dynamics from airborne LiDAR data based on selected features was validated and achieved.

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GEOMETRICAL CHARACTERIZATION OF KELVIN-LIKE METAL FOAMS FOR DIFFERENT STRUT SHAPES AND POROSITY

Journal of Porous Media ◽

10.1615/jpormedia.v18.i6.70 ◽

2015 ◽

Vol 18 (6) ◽

pp. 637-652 ◽

Cited By ~ 3

Author(s):

Prashant Kumar ◽

Frederic Topin ◽

Lounes Tadrist

Keyword(s):

Metal Foams ◽

Geometrical Characterization

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Characterization of bioaerosols in and around a landfill site in Delhi

Aerobiologia ◽

10.1007/s10453-021-09711-w ◽

2021 ◽

Author(s):

Arun Srivastava ◽

Richa Verma ◽

Dudun Mehta

Keyword(s):

Landfill Site

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A new approach to the bienergy and biangle of a moving particle lying in a surface of lorentzian space

International Journal of Nonlinear Sciences and Numerical Simulation ◽

10.1515/ijnsns-2019-0306 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Talat Körpınar ◽

Yasin Ünlütürk

Keyword(s):

Vector Fields ◽

Elastic Surface ◽

Geometrical Characterization ◽

Lorentzian Space ◽

New Approach ◽

Darboux Vector ◽

Moving Particle ◽

The Relationship ◽

Surface Curve

AbstractIn this research, we study bienergy and biangles of moving particles lying on the surface of Lorentzian 3-space by using their energy and angle values. We present the geometrical characterization of bienergy of the particle in Darboux vector fields depending on surface. We also give the relationship between bienergy of the surface curve and bienergy of the elastic surface curve. We conclude the paper by providing bienergy-curve graphics for different cases.

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Mass Spectrometry-Based Proteomic Characterization of Cutaneous Melanoma Ectosomes Reveals the Presence of Cancer-Related Molecules

International Journal of Molecular Sciences ◽

10.3390/ijms21082934 ◽

2020 ◽

Vol 21 (8) ◽

pp. 2934 ◽

Cited By ~ 1

Author(s):

Magdalena Surman ◽

Sylwia Kędracka-Krok ◽

Dorota Hoja-Łukowicz ◽

Urszula Jankowska ◽

Anna Drożdż ◽

...

Keyword(s):

Cell Proliferation ◽

Protein Content ◽

Cell Lines ◽

Cutaneous Melanoma ◽

Epithelial Mesenchymal Transition ◽

Mesenchymal Transition ◽

Starting Point ◽

Novel Biomarkers

Cutaneous melanoma (CM) is an aggressive type of skin cancer for which effective biomarkers are still needed. Recently, the protein content of extracellular vesicles (ectosomes and exosomes) became increasingly investigated in terms of its functional role in CM and as a source of novel biomarkers; however, the data concerning the proteome of CM-derived ectosomes is very limited. We used the shotgun nanoLC–MS/MS approach to the profile protein content of ectosomes from primary (WM115, WM793) and metastatic (WM266-4, WM1205Lu) CM cell lines. Additionally, the effect exerted by CM ectosomes on recipient cells was assessed in terms of cell proliferation (Alamar Blue assay) and migratory properties (wound healing assay). All cell lines secreted heterogeneous populations of ectosomes enriched in the common set of proteins. A total of 1507 unique proteins were identified, with many of them involved in cancer cell proliferation, migration, escape from apoptosis, epithelial–mesenchymal transition and angiogenesis. Isolated ectosomes increased proliferation and motility of recipient cells, likely due to the ectosomal transfer of different cancer-promoting molecules. Taken together, these results confirm the significant role of ectosomes in several biological processes leading to CM development and progression, and might be used as a starting point for further studies exploring their diagnostic and prognostic potential.

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