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

BOOST

Towards a Multi-agents Model for Automatic Big Data Processing to Support Urban Planningn planning. The huge amounts of collected data from different domains, such as urban management and remote sensing, are characterized as big data with a spatial component. Smart data is the approach to deal with big data characteristics and challenges by focusing on the Value aspect. The focus on sm

amenity

实现

谄媚于人

Enhancing the Management of Traffic Sequence Following Departure Trajectoriesganizations (such as the Euro control and International Civil Aviation Organization—ICAO) following their departure trajectories (the standard instrument departures—SIDs or omnidirectional trajectories), answering to the order of aircrafts’ demands of taxiing and taking off, especially when followin

Keratin

A Multiagent and Machine Learning Based Denial of Service Intrusion Detection System for Drone NetwoDoS) cyber-attacks targeting the networks of drones. The proposed model is autonomous, characterized by its high performance and enables the detection of known and unknown DoS attacks in UAV networks with high accuracy and low false-positives and false-negatives rates. This approach is intended to a

CLEAR

Rejuvenate

Fretful

Opportunities for Artificial Intelligence in Precision Agriculture Using Satellite Remote Sensing (AI). The huge amount of high-resolution remotely sensed data, the development of frameworks, and machine learning (ML) algorithms have made the analysis of raw data more advanced and precise. Artificial intelligence had unlocked a new perspective to solve sophisticated challenges in agriculture. T

HAWK

Apoptosis

Subimages-Based Approach for Landslide Susceptibility Mapping Using Convolutional Neural Networkes and propose solutions. An essential tool for landslide risk management is landslide susceptibility maps. In this paper, we developed a Convolutional Neural Network (CNN) model capable of producing a susceptibility map using seven explanatory variables: lithology, slope, drainage density, fault de