Titlebook: Geospatial Intelligence; Applications and Fut Fatimazahra Barramou,El Hassan El Brirchi,Youness Book 2022 The Editor(s) (if applicable) an

bourgeois

Subimages-Based Approach for Landslide Susceptibility Mapping Using Convolutional Neural Networknsity, elevation, roughness, and aspect. A susceptibility index map was generated in the Aknoul Region in the Rif to illustrate the CNN results. We found that areas with very high susceptibility index are affected the most by landslides.

MELD

https://doi.org/10.1007/978-3-642-80381-9ad Dataset, which is publicly available. The results obtained showed excellent performance in terms of recall, precision, accuracy, and F1 score, and they are very close to the ground truth; it outperforms all other models presented, with a high accuracy of 97.7%.

landfill

Gobble

Deep Convolution Neural Network for Automated Method of Road Extraction on Aerial Imageryad Dataset, which is publicly available. The results obtained showed excellent performance in terms of recall, precision, accuracy, and F1 score, and they are very close to the ground truth; it outperforms all other models presented, with a high accuracy of 97.7%.

dendrites

Toward a Deep Learning Approach for Automatic Semantic Segmentation of 3D Lidar Point Clouds in Urbaand other sources in conjunction with a Deep Learning technique whose objective is to automatically extract semantic information from airborne Lidar point clouds by enhancing both accuracy and semantic precision compared to the existing methods. We finally present the first results of our approach.

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muscle-fibers

The Conscious Agent and the Hope of Progressy cover, then estimating the dry biomass using a simple regression model that estimates the weight of a single rosemary tuff. The results are maps of the rosemary cover density where the random forest classifier gave high validation scores (67.5, 75.5, and 80%). The tuff weight estimator gave an accuracy of 7%.

Nuance

胆小懦夫

Creatinine-Test

Opportunities for Artificial Intelligence in Precision Agriculture Using Satellite Remote Sensingusing satellite remote sensing, and concern recent studies were conducted in the latest years 2019–2020. The accent was also pointed to the potential of AI in precision agriculture, the challenges, future needs, and trends in the field.