Ms. Gordana Jakovljevic Profile

Gordana Jakovljevic

at Univ of Banja Luka

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Publications (4)

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Proceedings Article | 21 September 2023 Paper

Optimization of extracted building footprints from UAV images

Miloš Tutnjević, Miro Govedarica, Gordana Jakovljević

Proceedings Volume 12786, 127860T (2023) https://doi.org/10.1117/12.2681315

KEYWORDS: Unmanned aerial vehicles, 3D modeling, Point clouds, RGB color model, Data modeling, Remote sensing, Atomic force microscopy, Orthophoto maps, Vegetation, Shadows

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Building footprints is important information in many types of applications, including optimization of rescuer response in case of catastrophic events, urban planning, urban dynamic monitoring, 3D building modeling etc. Traditionally, in remote sensing, building footprints are detected from very high-resolution images or point clouds. Convolution Neural Network (CNN) based semantic image segmentation model has become a common way to extract buildings footprints from remote sensing data with high accuracy regardless of differences in landscapes, shapes, texture, and used materials. However, the results of extraction usually represent rooftop outlines with overhangs rather than true building footprints. This paper presents the methodology for the optimization of building footprints by using contour information, which is derived from the UAV point cloud. First, the CNN model was used to extract rooftops from high-resolution UAV-based orthophoto. After that, the cross-section of the mesh model was performed in order to detect the outline of the building. The optimum height of the mesh cross section was derived based on an analysis of the Digital Elevation Model and Digital Surface Model. The generated results were compared with Open Street Map (OSM) and reference cadastral datasets. Quantitative and qualitative evaluations show that the proposed methodology can significantly improve the accuracy of CNN-extracted building footprints (and OSM data) compared to cadastral data. In addition, the high of buildings is simultaneously derived. Therefore, our approach opens up the possibility to use the full potential of UAV products for generating accurate building footprints and 3D building models of LoD1 with compatible accuracy as cadastral.

Proceedings Article | 27 June 2019 Paper

Monitoring spatial and temporal variation of water quality parameters using time series of open multispectral data

Miro Govedarica, Gordana Jakovljević

Proceedings Volume 11174, 111740Y (2019) https://doi.org/10.1117/12.2533708

KEYWORDS: Earth observing sensors, Landsat, Remote sensing, Satellites, Reflectivity, Near infrared, Sensors, Satellite imaging, Atmospheric modeling, Data modeling

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Water bodies are among most sensitive ecological environments. In order to ensure good water quality and establish framework for their protection European Parliament was adopted the Water Framework Directive (WFD) (Directive 2000/60/EC). The biological, hydro morphological and physic chemical quality parameters which are relevant for assessment of ecological status of water body are defined in Annex V of WFD. Traditionally, quality of surface water bodies are monitored by in situ measurements resulting in low spatial and temporal resolution of historical data. Remote sensing has great potential for monitoring and identification of water bodies over large scale regions in a more effective and efficient manner. In order to provide reliable monitoring of water quality, surface reflection derived by multispectral sensors need to be integrated with in situ measurements. Relationship between remote sensing and in situ data is usually modeled by using empirical, machine learning or deep learning algorithms. In this study, a 4-year (2013-2016) result of in situ monitoring of surface water bodies in Serbia are used for calibration and validation of algorithm for water quality monitoring based on Landsat 8 satellite image. The Turbidity, Suspending Sediments, Total Phosphorus and Total Nitrogen (physic chemical parameters) in region of Vojvodina, Republic of Serbia are monitored. The Neuron Networks and Supported Vector Machine are used to analyzing correlation between in situ measurements and Landsat 8 atmospherically corrected satellite images. Feature more, capabilities of Landsat 8 are compared with Sentinel 2 images (2-years, 2015-2016). In situ data are provided by Agency for environment protection of Serbia.

Proceedings Article | 10 October 2018 Paper

Flood risk assessment based on LiDAR and UAV points clouds and DEM

Miro Govedarica, Gordana Jakovljević, Flor Álvarez-Taboada

Proceedings Volume 10783, 107830B (2018) https://doi.org/10.1117/12.2513278

KEYWORDS: Clouds, LIDAR, Floods, Unmanned aerial vehicles

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Floods are one of the most serious and common natural disasters that cause fatalities and considerable economic losses worldwide every year. In order to reduce and manage risk that floods pose to human health, the environmental and economy flood risk map, as a crucial element of flood risk management, need to be generated. Most often flood risk is estimated based on Digital Elevation Model and projected water levels therefor DEM’s resolution and accuracy highly influence on the reliability of flood risk map especially in lowlands area, where the offset of few decimeters in the elevation data have a significant impact. Airborne light detection and ranging (LiDAR) remote sensing has been a widely used method that provides high-resolution topographical datasets. However LiDAR data are expensive and hard to acquire, usually limited by availability of technology and legal constrains. The main aim of this paper is to present usability of DEM, crated based on UAV RGB images, for flood risk assessment in Vojvodina Porvince, Republic of Serbia therefor flood risk assessment by using the UAV DEM was compared with a flood risk assessment based on LiDAR DEM. Additionally, UAV point cloud was compared with high resolution LiDAR point cloud.

Proceedings Article | 10 October 2018 Paper

Assessment of biological and physic chemical water quality parameters using Landsat 8 time series

Gordana Jakovljević, Miro Govedarica, Flor Álvarez-Taboada

Proceedings Volume 10783, 107831F (2018) https://doi.org/10.1117/12.2513277

KEYWORDS: Earth observing sensors, Landsat, Reflectivity, Satellites, Neurons, Near infrared, Remote sensing, Satellite imaging, Oxygen, Data modeling

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The Water Framework Directive of the European Union aims to protect water bodies from feature degradation. Monitoring is essential for assessment and comprehensive overview of water status. Annex V of WFD define tree type of water quality parameters which need to be monitored (biological and two supported one – hydro morphological and physic chemical) in order to assess ecological status of water bodies. Remote sensing data can be used for monitoring and identification of water bodies over large scale regions in a more effective and efficient manner. However, this technique must to be integrated with traditions in situ sampling method and field surveying in order to provide precise results. Various empirical, semi-analytics and machine learning algorithms exist to derive relationship between multi spectral image surface reflectance and water quality indicators derived from in situ measurement. In this study we evaluate the capabilities of Landsat 8 satellite image for assessment of abundance of phytoplankton’s (biological parameters) and Turbidity, Dissolved oxygen, Total Phosphorus and Total Nitrogen (physic chemical parameters) in region of Vojvodina, Republic of Serbia. The Neuron Networks are used to analyzing correlation between in situ measurements and 7 Landsat 8 atmospherically corrected satellite images acquired in 2013. In situ data are obtained from Agency for environment protection of Serbia. Our results shows that satellite-based monitoring, in combination with in situ data, provide an improved basis for more effective monitoring of large number of water bodies over large geographical area. Relationship between derived and WFD quality parameters is established in order to provide usage of remote sensing data for ecological status classification according to WFD.

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