The Usage of Artificial Intelligence in Remote Sensing: A Review of Applications and Current Research

1988 ◽  
pp. 489-512 ◽  
Author(s):  
Andrea G. Fabbri ◽  
Ko B. Fung ◽  
Susan M. Yatabe
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing

scholarly journals Editorial for the Special Issue “Advanced Artificial Intelligence and Deep Learning for Remote Sensing”

2021 ◽  
Vol 13 (15) ◽  
pp. 2883
Author(s):  
Gwanggil Jeon
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Deep Learning ◽  
Special Issue ◽  
The Earth

Remote sensing is a fundamental tool for comprehending the earth and supporting human–earth communications [...]

scholarly journals APPLICATIONS OF COMPUTER VISION IN AGRICULTURE

2021 ◽  
Vol 5 (9) ◽  
Author(s):  
Pranav Ghadge ◽  
Riddhik Tilawat ◽  
Prasanna Sand ◽  
Parul Jadhav
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Computer Vision ◽  
Educational Technology ◽  
Satellite System ◽  
High Quality ◽  
Agricultural Applications ◽  
Efficient Processing

Satellite system advances, remote sensing and drone technology are continuing. These progresses produce high-quality images that need efficient processing for smart agricultural applications. These possibilities to merge computer vision and artificial intelligence in agriculture are exploited with recent deep educational technology. This involves essential phenomena of data and huge quantities of data stored, analysed and used when making decisions. This paper demonstrates how computer vision in agriculture can be used.

REVISITING THE METHODOLOGICAL DEVELOPMENT IN SOIL EROSION RESEARCH

Ensemble ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 145-165
Author(s):  
Tanmoy Sarkar ◽  
◽  
Tapas Pal ◽  
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Soil Erosion ◽  
Geographic Information ◽  
Methodological Development ◽  
Advantages And Disadvantages ◽  
The Future ◽  
Soil Erosion By Water

Soil erosion (by water) is a major land degradation process that may threat the Sustainable Development Goals (SDG) by its negative impact on environment and human well-being. Soil erosion research demands scientific methods, tools and techniques to assess soil erosion with more accuracy and reliability. Soil erosion research has had experienced crude field-based techniques in early twentieth century to model-based approaches since the 1970s and very recent machine learning and artificial intelligence models to predict soil erosion susceptibility and risk. The paper aims to review the trend in methodological development in soil erosion by water through time. The brief background of different approaches, their relative advantages and disadvantages are reviewed. Depending on the time of establishment and wide application the approaches are classified and represented as erosion plot/runoff approach, erosion pin technique followed by environmental tracer method and model approach in combination with Remote Sensing (RS) and Geographic Information System (GIS). Recent advancement in artificial intelligence and application of statistical techniques have a great potential to contribute in soil erosion research by identifying various degrees of susceptibility in large scale and also to quantify the erosion rate with high accuracy. The Remote sensing (RS) and Geographic Information System (GIS) contribute to develop regional scale data base with exploration of real time data and spatial analysis. The combination of RS & GIS and process-based models must be more effective than the traditional soil erosion model in the context of prediction with greater reliability and validity. The future research on soil erosion is better to focus on the theoretical analysis and development of erosion prediction model with more quantitative refinement and to model the future.

scholarly journals Research on Quantitative Remote Sensing Monitoring Algorithm of Air Pollution Based on Artificial Intelligence

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Yun Liu ◽  
Yuqin Jing ◽  
Yinan Lu
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Air Pollution ◽  
Transmission Model ◽  
Atmospheric Radiation ◽  
Remote Sensing Monitoring ◽  
Quantitative Remote Sensing ◽  
Monitoring Accuracy ◽  
Radiation Transmission ◽  
Monitoring Algorithm

When the current algorithm is used for quantitative remote sensing monitoring of air pollution, it takes a long time to monitor the air pollution data, and the obtained range coefficient is small. The error between the monitoring result and the actual result is large, and the monitoring efficiency is low, the monitoring range is small, and the monitoring accuracy rate is low. An artificial intelligence-based quantitative monitoring algorithm for air pollution is proposed. The basic theory of atmospheric radiation transmission is analyzed by atmospheric radiation transfer equation, Beer–Bouguer–Lambert law, parallel plane atmospheric radiation theory, atmospheric radiation transmission model, and electromagnetic radiation transmission model. Quantitative remote sensing monitoring of air pollution provides relevant information. The simultaneous equations are constructed on the basis of multiband satellite remote sensing data through pixel information, and the aerosol turbidity of the atmosphere is calculated by the equation decomposition of the pixel information. The quantitative remote sensing monitoring of air pollution is realized according to the calculated aerosol turbidity. The experimental results show that the proposed algorithm has high monitoring efficiency, wide monitoring range, and high monitoring accuracy.

Artificial intelligence and data analytics for geosciences and remote sensing

2021 ◽  
pp. 1055-1082
Author(s):  
Feras Al-Obeidat ◽  
Farhi Marir ◽  
Fares M. Howari ◽  
Abdel-Mohsen O. Mohamed ◽  
Neil Banerjee
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Data Analytics

Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches

2016 ◽  
Vol 37 (23) ◽  
pp. 5605-5631 ◽  
Author(s):  
Vahid Moosavi ◽  
Ali Talebi ◽  
Mohammad Hossein Mokhtari ◽  
Mohammad Reza Hadian
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Soil Moisture

Monitoring of levee breaching through remote sensing and artificial intelligence

2020 ◽  
Author(s):  
Rosa Di Maio ◽  
Eleonora Vitagliano ◽  
Rosanna Salone
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Time Series ◽  
Soil Properties ◽  
Remote Sensing Data ◽  
Ground Level ◽  
Model Parameters ◽  
Levee Failure ◽  
Geophysical Surveys ◽  
Derived Data

<p>The study of flooding events resulting from bank over-flooding and levee breaching is of large interest for both society and environment, because flood waves, resulting from levee failure, might cause loss of lives and destruction of properties and ecosystems. Understanding the subsoil mechanics and the fluid-solid interplay allows the stability condition estimate of dams, embankments and slopes and the development of early warning alarm systems. Changes in soil and hydraulic parameters are usually monitored by geotechnical and geophysical investigations that also provide the basic assumptions for developing hydraulic models. Nowadays, remote sensing approaches, including satellite techniques, are mainly used for flooding simulation studies. Indeed, remote sensing observations, such as discharge, flood area extent and water stage, have been used for retrieving flood hydrology information and modeling, calibrating and validating hydrodynamic models, improving model structures and developing data assimilation models. Although all these studies have contributed significantly to the recent advances, uncertainty in observations, as well as in model parameters and prediction, represents a critical aspect for using remote sensing data in the flooding defence. Compared to past and current methods for monitoring the fluvial levee failure, we propose a new procedure that provides a wide and fast alert system. The proposed methodological path is based on presumed relationships between ground level deformation and hydrological and surface soil properties, due to physical mechanisms and exhibited by geodetic and hydrological time series. The procedure is accomplished first through multi-methodological comparative analyses applied to geodetic, hydrological and soil-properties patterns, then through the mapping of the river zones prone to failure. Since the input consists of time series satellite-derived data, the geospatial Artificial Intelligence is applied for extracting knowledge from spatial big data and for increasing the performance of data computing. In particular, machine learning is initially developed for selecting the relevant geographical areas (i.e. rivers, levees and riverbanks) from large geo-referential datasets. Then, since the spatial-distributed points are also time-dependent, the trends of different datasets are compared point by point by selected analytical techniques. Finally, in accordance with the acquired knowledge from previous steps, the system extracts information on the correlation indexes in order to make sense of patterns in space and time and to identify hierarchic orders for the realization of hazard maps. The proposed method is “wide” because, unlike other direct surveys, it is able to monitor large spatial areas since it is based on satellite-derived data. It is also “fast” because it is based on the Earth’s surface observation and is not connected with Earth’s inland investigations (such as the geotechnical and geophysical ones) or with forecasting models (e.g. hydraulic and flooding simulations). Due to these peculiarities, the method can support flood protection studies and can be used for driving the localization of river portions prone to failure, where focusing detailed geotechnical and geophysical surveys.</p>

APPLICATIONS OF ARTIFICIAL INTELLIGENCE TECHNIQUES TO REMOTE SENSING∗

1986 ◽  
Vol 38 (2) ◽  
pp. 133-141 ◽  
Author(s):  
John E. Estes ◽  
Charlene Sailer ◽  
Larry R. Tinney
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Artificial Intelligence Techniques

scholarly journals Applications of Artificial Intelligence for Precision Agriculture

EDIS ◽  
2018 ◽  
Vol 2018 (6) ◽  
Author(s):  
Yiannis Ampatzidis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Remote Sensing ◽  
Computer Vision ◽  
Precision Agriculture ◽  
Agricultural Systems ◽  
Disease Identification ◽  
Technological Advances

Technological advances in computer vision, mechatronics, artificial intelligence and machine learning have enabled the development and implementation of remote sensing technologies for plant/weed/pest/disease identification and management. They provide a unique opportunity for developing intelligent agricultural systems for precision applications. Herein, the Artificial Intelligence (AI) and Machine Learning concepts are described, and several examples are presented to demonstrate the application of the AI in agriculture. Available on EDIS at: https://edis.ifas.ufl.edu/ae529

Sign in / Sign up

Export Citation Format

Share Document