Academic Articles & Research Papers

Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applicatio...

Diversity in Machine Learning

Machine learning methods have achieved good performance and been widely applied in various real-world applications. They can learn the model adaptively and be better fit for special requirements of different tasks. Generally, a good machine...

A Sensor-to-API Data Infrastructure for Building Digital Twins: Harmonizing Multi-Rate IoT Energy and Indoor Data Streams for Proactive Analytics

Digital twins are increasingly used in smart buildings to monitor energy use. This requires reliable sensor data, geospatial information, and semantically rich 3D building geometry. However, building sensor data often has inconsistent sampl...

Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model

With the implementation of the 2018–2020 Clean Air Action Plan (CAAP) the and impact from COVID-19 lockdowns in 2020, air pollution emissions in central and eastern China have decreased markedly. Here, by combining satellite remote sensing,...

Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures Based on Sentinel-1, Sentinel-2 and Meteorological Data Using Multiple Data Transformations

Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass express...

Integrating InVEST and machine learning to model mangrove habitat degradation trend in in Northern Persian Gulf, Iran

Abstract The expansion of human activities and environmental changes in coastal desert and semi-desert ecosystems significantly impact habitat quality. This study used remote sensing data on the Google Earth Engine (GEE) platform to generat...

Machine Learning for Climate Precipitation Prediction Modeling over South America

Many natural disasters in South America are linked to meteorological phenomena. Therefore, forecasting and monitoring climatic events are fundamental issues for society and various sectors of the economy. In the last decades, machine learni...

Machine learning in geosciences and remote sensing

Learning incorporates a broad range of complex procedures. Machine learning (ML) is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine...

Aerosol Optical Depth Retrieval from MODIS Using a Physically Informed Machine Learning Framework

Retrieving aerosol optical depth (AOD) over land remains challenging because the relatively weak aerosol signal in top-of-atmosphere (TOA) observations must be separated from strong and spatially heterogeneous surface reflectance. Here, we ...

Surface circulation properties in the eastern Mediterranean emphasized using machine learning methods

The eastern Mediterranean surface circulation is highly energetic and composed of structures interacting stochastically. However, some main features are still debated, and the behavior of some fine-scale dynamics and their role in shaping t...

Multi-Resolution and Multi-Temporal Satellite Remote Sensing Analysis to Understand Human-Induced Changes in the Landscape for the Protection of Cultural Heritage: The Case Study of the MapDam Project, Syria

This study presents a multi-resolution and multi-temporal remote sensing approach to assess human-induced changes in cultural landscapes, with a focus on the archaeological site of Amrit (Syria) within the MapDam project. By integrating sat...

Leveraging AI to Estimate Caribou Lichen in UAV Orthomosaics from Ground Photo Datasets

Relating ground photographs to UAV orthomosaics is a key linkage required for accurate multi-scaled lichen mapping. Conventional methods of multi-scaled lichen mapping, such as random forest models and convolutional neural networks, heavily...

Assessing climate and land use impacts on surface water yield using remote sensing and machine learning

Abstract Climate and land use changes are critical factors affecting watershed water yields, with significant implications for water resources at both local and regional levels. This study examined the combined effects of temporal and spati...

SSMSFuse: A Spectral and Spatial Multiscale Coupling Fusion Model for Hyperspectral and Multispectral Image

Hyperspectral image (HSI) has more spectral information than conventional images, which helps to distinguish targets in a complex scene more accurately. However, HSI typically has a low spatial resolution, which limits their application sce...

SEMANTIC URBAN MESH SEGMENTATION BASED ON AERIAL OBLIQUE IMAGES AND POINT CLOUDS USING DEEP LEARNING

The use of deep machine learning methods for semantic classification of city mesh models is one of the current trends in geoscience development. Thanks to the thriving development of Convolutional Neural Networks (CNNs) it is now achievable...

Happiness Index Determination by Analyzing Satellite Images for Urbanization

Waterbody identification from satellite images in an automated manner is one of the difficult tasks in the domain of Remote Sensing (RS). In recent years, several image processing approaches have been developed to process RGB or multispectr...

Unmanned Aerial Vehicle for Precision Agriculture: A Review

Digital Precision Agriculture (DPA) is a comprehensive approach to agronomic management that utilizes advanced technologies, such as sensor data analysis and automation, to optimize crop productivity, enhance farm income, and minimize envir...

Assessing carbon and economic loss from oak decline in Iran’s Zagros forests using Sentinel-2 and machine learning

Over recent decades, the decline of oak trees, which has emerged as the most significant challenge to the Zagros forests (ZFs), poses serious threats to carbon storage (CS) and economic valuation, jeopardizing the ecological and economic su...

Spatio-temporal assessment of Toumeyella parvicornis spread in mediterranean coastal pine forests through sentinel-2 data

Mediterranean coastal pine forests offer vital ecological and cultural benefits but face threats from climate change, urbanization, and pests. Among invasive agents, Toumeyella parvicornis is a severe threat to Pinus pinea along Italy's coa...

Physically interpretable feature learning and model robustness analysis for remote sensing LAI retrieval.