NaturaSat—A Software Tool for Identification, Monitoring and Evaluation of Habitats by Remote Sensing Techniques

The NaturaSat software integrates various image processing techniques together with vegetation data, into one multipurpose tool that is designed for performing facilities for all requirements of habitat exploration, all in one place. It provides direct access to multispectral Sentinel-2 data provided by the European Space Agency. It supports using these data with various vegetation databases, in a user-friendly environment, for, e.g., vegetation scientists, fieldwork experts, and nature conservationists. The presented study introduces the NaturaSat software, describes new powerful tools, such as the semi-automatic and automatic segmentation methods, and natural numerical networks, together with validated examples comparing field surveys and software outputs. The software is robust enough for field work researchers and stakeholders to accurately extract target units’ borders, even on the habitat level. The deep learning algorithm, developed for habitat classification within the NaturaSat software, can also be used in various research tasks or in nature conservation practices, such as identifying ecosystem services and conservation value. The exact maps of the habitats obtained within the project can improve many further vegetation and landscape ecology studies.

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On the Use of Tri-Stereo Pleiades Images for the Morphometric Measurement of Dolines in the Basaltic Plateau of Azrou (Middle Atlas, Morocco)

Remote Sensing ◽

10.3390/rs13204087 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4087

Author(s):

Maria Teresa Melis ◽

Luca Pisani ◽

Jo De Waele

Keyword(s):

Remote Sensing ◽

Satellite Image ◽

European Space Agency ◽

Morphometric Study ◽

Stereo Images ◽

Space Agency ◽

Middle Atlas ◽

Mapping Techniques ◽

Remote Sensing Techniques ◽

Basaltic Plateau

Hundreds of large and deep collapse dolines dot the surface of the Quaternary basaltic plateau of Azrou, in the Middle Atlas of Morocco. In the absence of detailed topographic maps, the morphometric study of such a large number of features requires the use of remote sensing techniques. We present the processing, extraction, and validation of depth measurements of 89 dolines using tri-stereo Pleiades images acquired in 2018–2019 (the European Space Agency (ESA) © CNES 2018, distributed by Airbus DS). Satellite image-derived DEMs were field-verified using traditional mapping techniques, which showed a very good agreement between field and remote sensing measures. The high resolution of these tri-stereo images allowed to automatically generate accurate morphometric datasets not only regarding the planimetric parameters of the dolines (diameters, contours, orientation of long axes), but also for what concerns their depth and altimetric profiles. Our study demonstrates the potential of using these types of images on rugged morphologies and for the measurement of steep depressions, where traditional remote sensing techniques may be hindered by shadow zones and blind portions. Tri-stereo images might also be suitable for the measurement of deep and steep depressions (skylights and collapses) on Martian and Lunar lava flows, suitable targets for future planetary cave exploration.

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Using geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs

GEOMATICA ◽

10.1139/geomat-2020-0010 ◽

2021 ◽

pp. 1-23

Author(s):

Roholah Yazdan ◽

Masood Varshosaz ◽

Saied Pirasteh ◽

Fabio Remondino

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Automatic Segmentation ◽

Geometric Constraints ◽

Support Vector ◽

Svm Classifier ◽

Data Set ◽

Traffic Signs ◽

Deep Learning Algorithm ◽

Comparison Results

Automatic detection and recognition of traffic signs from images is an important topic in many applications. At first, we segmented the images using a classification algorithm to delineate the areas where the signs are more likely to be found. In this regard, shadows, objects having similar colours, and extreme illumination changes can significantly affect the segmentation results. We propose a new shape-based algorithm to improve the accuracy of the segmentation. The algorithm works by incorporating the sign geometry to filter out the wrong pixels from the classification results. We performed several tests to compare the performance of our algorithm against those obtained by popular techniques such as Support Vector Machine (SVM), K-Means, and K-Nearest Neighbours. In these tests, to overcome the unwanted illumination effects, the images are transformed into colour spaces Hue, Saturation, and Intensity, YUV, normalized red green blue, and Gaussian. Among the traditional techniques used in this study, the best results were obtained with SVM applied to the images transformed into the Gaussian colour space. The comparison results also suggested that by adding the geometric constraints proposed in this study, the quality of sign image segmentation is improved by 10%–25%. We also comparted the SVM classifier enhanced by incorporating the geometry of signs with a U-Shaped deep learning algorithm. Results suggested the performance of both techniques is very close. Perhaps the deep learning results could be improved if a more comprehensive data set is provided.

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AUTOMATIC SEGMENTATION AND INDICATORS MEASUREMENT OF THE VOCAL FOLDS AND GLOTTAL IN LARYNGEAL ENDOSCOPY IMAGES USING MASK R-CNN

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237221500277 ◽

2021 ◽

pp. 2150027

Author(s):

Chiun-Li Chin ◽

Chun-Lung Chang ◽

Yu-Chieh Liu ◽

Yong-Long Lin

Keyword(s):

Clinical Practice ◽

Medical Information ◽

Learning Algorithm ◽

Automatic Segmentation ◽

Vocal Folds ◽

Positive Influence ◽

High Rate ◽

Deep Learning Algorithm ◽

Proposed Model ◽

Clinic Practice

In present clinic practice of otolaryngology, otolaryngologists utilized laryngoscopy to diagnose the larynx lesion of patients preliminarily. Nevertheless, it was challenging for otolaryngologists to interpret the detailed information from laryngoscopy videos comprehensively. In this paper, we proposed Mask R-CNN deep learning algorithm to segment the regions of the vocal folds and glottal from laryngoscopy videos, and self-built algorithm to calculate measured indicators including the length and curvature of vocal folds, the angle of glottal, the area of vocal folds and glottal, and the triangle type composed of vocal folds and glottal. Moreover, in order to provide otolaryngologists critical and immediate medical information during diagnosis, we also provided visualized information, which is labeled on the laryngoscopy images to meet all the needs in clinical practice. From the result of this research, the precision of segmentation has reached a high rate of 90.4% on average. It shows that the model not only achieves great performance in segmentation, but also further proved the indicators are accurate enough to be considered in practical diagnosis. In the future, it is possible for the proposed model to be applied in more kinds of laryngoscopy analyses for more comprehensive diagnosis, which would make a positive influence toward the clinical practice of otolaryngology.

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LAND SUBSIDENCE ANALYSIS CAUSED BY AQUIFER OVEREXPLOITATION USING GEP TOOLS: A-DINSAR ON THE CLOUD

10.4995/cigeo2021.2021.12722 ◽

2021 ◽

Author(s):

Guadalupe Bru ◽

Pablo Ezquerro ◽

Carolina Guardiola-Albert ◽

Marta Béjar-Pizarro ◽

Gerardo Herrera ◽

...

Keyword(s):

Remote Sensing ◽

Water Stress ◽

Land Subsidence ◽

Satellite Remote Sensing ◽

European Space Agency ◽

Space Agency ◽

Surface Displacements ◽

Subsidence Analysis ◽

Remote Sensing Techniques ◽

Aquifer Overexploitation

Groundwater is a vitally important resource for humans. One of the main problems derived from the overexploitation ofaquifers is land subsidence, which in turn carries other associated natural risks. Advanced Differential satellite radarinterferometry (A-DInSAR) techniques provide valuable information on the surface displacements of the ground, whichserve to characterize both the deformational behaviour of the aquifer and its properties. RESERVOIR is a research projectbelonging to the European PRIMA programme, whose main objective is to design sustainable groundwater managementmodels through the study of four areas of the Mediterranean subjected to water stress. One of the main tasks of the projectis the integration of the terrain deformation data obtained with satellite remote sensing techniques in the hydrogeologicaland geomechanical models of the aquifers. In the present work, a first evaluation of the deformation of the ground in eachstudy area is carried out using the tools contained in the Geohazards Exploitation Platform (GEP). This is a service financedby the European Space Agency (ESA) that allows processing directly on its server, without need to store data orapplications locally.

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