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Abstract

Land use/land cover (LULC) maps are important datasets in various environmental projects. Our aim was to demonstrate how GEOBIA framework can be used for integrating different data sources and classification methods in context of LULC mapping.We presented multi-stage semi-automated GEOBIA classification workflow created for LULC mapping of Tuszyma Forestry Management area based on multi-source, multi-temporal and multi-resolution input data, such as 4 bands- aerial orthophoto, LiDAR-derived nDSM, Sentinel-2 multispectral satellite images and ancillary vector data. Various classification methods were applied, i.e. rule-based and Random Forest supervised classification. This approach allowed us to focus on classification of each class ‘individually’ by taking advantage from all useful information from various input data, expert knowledge, and advanced machine-learning tools. In the first step, twelve classes were assigned in two-steps rule-based classification approach either vector-based, ortho- and vector-based or orthoand Lidar-based. Then, supervised classification was performed with use of Random Forest algorithm. Three agriculture-related LULC classes with vegetation alternating conditions were assigned based on aerial orthophoto and Sentinel-2 information. For classification of 15 LULC classes we obtained 81.3% overall accuracy and kappa coefficient of 0.78. The visual evaluation and class coverage comparison showed that the generated LULC layer differs from the existing land cover maps especially in relative cover of agriculture-related classes. Generally, the created map can be considered as superior to the existing data in terms of the level of details and correspondence to actual environmental and vegetation conditions that can be observed in RS images.
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Authors and Affiliations

Anna Osmólska
Paweł Hawryło
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Abstract

From a management perspective, water quality is determined by the desired end use. Water intended for leisure, drinking water, and the habitat of aquatic organisms requires higher levels of purity. In contrast, the quality standards of water used for hydraulic energy production are much less important.
The main objective of this work is focused on the development of an evaluation system dealing with supervised classification of the physicochemical quality of the water surface in the Moulouya River through the use of artificial intelligence. A graphical interface under Matlab 2015 is presented. The latter makes it possible to create a classification model based on artificial neural networks of the multilayer perceptron type (ANN-MLP).
Several configurations were tested during this study. The configuration [9 8 3] retained gives a coefficient of determination close to the unit with a minimum error value during the test phase.
This study highlights the capacity of the classification model based on artificial neural networks of the multilayer perceptron type (ANN-MLP) proposed for the supervised classification of the different water quality classes, determined by the calculation of the system for assessing the quality of surface water (SEQ-water) at the level of the Moulouya River catchment area, with an overall classification rate equal to 98.5% and a classification rate during the test phase equal to 100%.
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Authors and Affiliations

Imad Manssouri
1
ORCID: ORCID
Abdelghani Talhaoui
2
Abdellah El Hmaidi
2
ORCID: ORCID
Brahim Boudad
3
Bouchra Boudebbouz
1
Hassane Sahbi
4

  1. Moulay Ismail University, National School of Arts and Crafts, Laboratory of Mechanics, Mechatronics, and Command, Team of Electrical Energy, Maintenance and Innovation, Meknes, Marjane 2, BP: 298 Meknes 50050, Morocco
  2. Moulay Ismail University, Faculty of Sciences, Water Sciences and Environmental Engineering team, Meknes, Morocco
  3. Moulay Ismail University, Faculty of Sciences, Department of Geology, Laboratory of Geo-Engineering and Environment, Meknes, Morocco
  4. Moulay Ismail University, Meknes, Morocco
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Abstract

Sorting coal and gangue is important in raw coal production; accurately identifying coal and gangue is a prerequisite for effectively separating coal and gangue. The method of extracting coal and gangue using image grayscale information can effectively identify coal and gangue, but the recognition rate of the sorting process based on image grayscale information needs to substantially higher than that which is needed to meet production requirements. A sorting method of coal and gangue using object surface grayscale-gloss characteristics is proposed to improve the recognition rate of coal and gangue. Using different comparative experiments, bituminous coal from the Huainan area was used as the experimental object. It was found that the number of pixel points corresponding to the highest level grey value of the grayscale moment and illumination component of the coal and gangue images were combined into a total discriminant value and used as input for the best classification of coal and gangue using the GWO-SVM classification model. The recognition rate could reach up to 98.14%. This method sorts coal and gangue by combining surface greyness and glossiness features, optimizes the traditional greyness-based recognition method, improves the recognition rate, makes the model generalizable, enriches the research on coal and gangue recognition, and has theoretical and practical significance in enterprise production operations.
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Authors and Affiliations

Gang Cheng
1
Yifan Wei
1 2
ORCID: ORCID
Jie Chen
1
Zeye Pan
1

  1. School of Mechanical Engineering, Anhui University of Science and Technology, Huainan, China
  2. State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines,Anhui University of Science and Technology, Huainan, China

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