Datasets & Opensources
In-Situ Chl-a Concentrations and Co-Located Atmospherically Corrected Satellite Products for Western Lake Ontario from 2000-2022
This dataset encompasses in-situ chlorophyll-a (Chl-a) concentrations and co-located atmospherically corrected pixel values from Landsat 5, Landsat 7, Landsat 8, and Sentinel-2 satellites between 2000 and 2022, enabling remote sensing model development.
Region: Western Lake Ontario, Canada.
Prepared by: A. R. Shahvaran, H. Kheyrollah Pour, P. Van Cappellen
Water Institute, University of Waterloo & Wilfrid Laurier University
For more information, please visit: https://doi.org/10.20383/102.0713
Water Quality Measurements from Western Lake Ontario with Synchronous Co-located Multispectral UAV and Satellite Data from 2022-06-14
This dataset provides in-situ measurements for water quality parameters like chlorophyll-a (Chl-a) and Total Suspended Solids (TSS), alongside co-located multispectral pixel values from a UAV-mounted MicaSense RedEdge-MX and the Landsat 9 OLI-2 sensor.
Region: Western Lake Ontario, Canada.
Prepared by: A. R. Shahvaran, H. Kheyrollah Pour, S. Slowinski, A. Maclean, F. Rezanezhad, P. Van Cappellen
Water Institute, University of Waterloo & Wilfrid Laurier University
For more information, please visit: https://doi.org/10.20383/103.0796
Time-Series Analysis of RS-Derived Chl-a Concentrations from 2013 to 2023 in Western Lake Ontario Using Landsat 8-9 Imagery
This dataset offers a collection of match-up data and code for preprocessing, processing, and analyzing satellite imagery to conduct a time-series analysis of Remote Sensing (RS) derived chlorophyll-a (Chl-a) maps.
Region: Western Lake Ontario and Hamilton Harbour, Canada.
Prepared by: A. R. Shahvaran, H. Kheyrollah Pour, C. Binding, P. Van Cappellen
Water Institute, University of Waterloo; Wilfrid Laurier University; Environment and Climate Change Canada
For more information, please visit: https://doi.org/10.20383/103.0896
Data and Analysis Code for Long-Term Trend and Correlation Analysis of Daymet Shortwave Radiation, CERES Solar Insolation, ERA5 Total Cloud Cover, MODIS Cloud Fraction, In-situ Cloud Cover Observations, and Landsat Lake Surface Water Temperature in Northwest Territories (NWT), Canada (1980–2023)
This dataset comprises processed data and accompanying code for preprocessing, processing, and analyzing multiple climate and environmental variables to assess long-term changes and surface water temperatures.
Region: Northwest Territories (NWT), Canada.
Prepared by: B. D. Persaud, A. R. Shahvaran, G. Attiah, L. Chasmer, M. English, H. Kheyrollah Pour, B. Wolfe
Water Institute, University of Waterloo & Wilfrid Laurier University
For more information, please visit: https://doi.org/10.20383/103.01165
High-Resolution (1 km) Daily Climate Gridded Dataset for Guyana (1950–2024)
This dataset provides a high-resolution (1 km) gridded analysis of daily total precipitation and daily mean air temperature spanning 1950–2024. The data were derived from ERA5-Land reanalysis, processed via Google Earth Engine (GEE), and downscaled using machine learning interpolation.
Region: Guyana.
Prepared by: B. D. Persaud, G. Attiah, M. Kalamandeen, A. R. Shahvaran, J. Radosavljevic, L. Alves, E. Hamer, G. Maharaj, R. Hastings, H. Kheyrollah Pour, P. Van Cappellen
Water Institute, University of Waterloo; Wilfrid Laurier University; University of Guyana
For more information, please visit: https://doi.org/10.20383/103.01185
Forest Fire Risk Mapping & Simulation System
This project is a Python-based GIS wildfire simulation platform focused on forest regions of Siberia, Russia. Developed by Mr. Kharlamov Ilia, and supervised by Prof. Saied Pirasteh at the Institute of Artificial Intelligence, Shaoxing University, extracted from the GIS Intelligence Course.
For more information or downloading the datasets, please visit: https://github.com/kharlamovilya/syberian-fire-prediction
Leafmap
#Leafmap now supports downloading Google Open Buildings for any country with only one line of code. It can automatically download all tiles and merge them as a single vector file.
BloodStain_Identification_using_ViT
This model refers to a Hybrid Vision Transformer for Hyperspectral Imaging-based Bloodstain Classification, which was developed by Mr. Muhammad Hassaan Farooq Butt.
For more information, please visit: https://github.com/MHassaanButt/BloodStain_Identification_using_ViT.
Capacity Building and Education
Datasets for the Shaoxing University student achievement dataset and the MIT-BIH Arrhythmia database. Developed by Associate Professor Sheng Feng, Institute of Artificial Intelligence, Shaoxing University.
For more information or downloading the datasets, please visit: https://github.com/fengsheng13/datasets
Water Segmentation Datasets
Prepared by:
Armin Moghimi
Ludwig Franzius Institute of Hydraulic, Estuarine and Coastal Engineering
Leibniz University Hannover
For more information, please visit:
https://www.kaggle.com/datasets/arminmoghimi/lufi-riversnap
Tensor-based Relative Radiometric Normalization (RRN) and its optimization using TRR and GA (codes and dataset)
Prepared by:
Armin Moghimi
Ludwig Franzius Institute of Hydraulic, Estuarine and Coastal Engineering
Leibniz University Hannover
moghimi@lufi.uni-hannover.de
Link for Tensor-based-keypoint-detection
https://github.com/ArminMoghimi/Tensor-based-keypoint-detection
Keypoint-based Relative Radiometric Normalization (RRN)
Prepared by:
Armin Moghimi
Ludwig Franzius Institute of Hydraulic, Estuarine and Coastal Engineering
Leibniz University Hannover
moghimi@lufi.uni-hannover.de
For more information, please visit: https://github.com/ArminMoghimi/keypoint-based-RRN
The Edge-Aware MRF (EAMRF) Forest Change Detection Method
Prepared by:
Armin Moghimi
Ludwig Franzius Institute of Hydraulic, Estuarine and Coastal Engineering
Leibniz University Hannover
moghimi@lufi.uni-hannover.de
For more information, please visit: https://github.com/ArminMoghimi/The-EAMRF-forest-change-detection-method
Remote Sensing-Enhanced Spatio-Temporal Model for Precision Prediction of Anopheles Larval Habitats
Prepared by:
Fahimeh Youssefi
Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K.N.Toosi University, Tehran, Iran.
Institute of Artificial Intelligence, Shaoxing University, 508 West Huancheng Road, Yuecheng District, Shaoxing, Zhejiang Province, Postal Code 312000, China.
youssefi@email.kntu.ac.ir
youssefi@email.usx.edu.cn
DOI: 10.5281/zenodo.11081690
Visit https://zenodo.org/records/11081690?token=eyJhbGciOiJIUzUxMiIsImlhdCI6MTcxNDM2MDkxMSwiZXhwIjoxNzk4NTg4Nzk5fQ.eyJpZCI6IjczNjE2MWU4LTg4NmMtNGJlNC1hZjQwLWIwM2I4ZTI5M2EwNCIsImRhdGEiOnt9LCJyYW5kb20iOiJiMTAyNGQ1YmNmOTcwMTRmZGZlODE1YzVkN2YzNjg3MCJ9.rmW7FluXlaeyd0Y4SaWCciXz0fCbj6HoKFT5c53QWireTJVmQXrUG18eULSdeUBsJnSTIjfCycFTRGFrRiGurA
for datasets and more information.
Landslide Detection: Faster-RCNN
Prepared by:
Southwest Jiaotong University: Team & Saied Pirasteh
email: sapirasteh1@usx.edu.cn
The Copernicus Land Monitoring Service
The Copernicus Land Monitoring Service, also known as simply CLMS, is one of six thematic Copernicus services.
For more information, please visit: https://land.copernicus.eu/en/dataset-catalog
Tensor-based Relative Radiometric Normalization (RRN) and its optimization using TRR and GA
This repository contains MATLAB codes and datasets utilized for relative radiometric normalization (RRN) of bi-temporal multi-spectral images in the following papers:
Armin Moghimi, Turgay Celik & Ali Mohammadzadeh (2022) Tensor-based keypoint detection and switching regression model for relative radiometric normalization of bitemporal multispectral images, International Journal of Remote Sensing, 43:11, 3927-3956, DOI: 10.1080/01431161.2022.2102951) Armin Moghimi, Turgay Celik, Ali Mohammadzadeh, Saied Pirasteh, and Jonathan Li (IGARS IEEE 2024), Optimizing Relative Radiometric Modeling: Fine-tuning strategies using Trust-Region Reflective and Genetic Algorithms for Residual Error Minimization.
This datasets are available at: https://github.com/ArminMoghimi/Tensor-based-keypoint-detection
A-Transfer-Learning-Network-for-Earthquake-induced-Landslide-Remote-Sensing-Image-Detection
Shaoqiang Meng, Zhenming Shi, Saied Pirasteh, Silvia Liberata Ullo, Ming Peng, Changshi Zhou, Limin Zhang, Wesley Nunes Gonçalves
For code:
https://github.com/mengshaoqiang/A-Transfer-Learning-Network-for-Earthquake-induced-Landslide-Remote-Sensing-Image-Detection
For dataset:
http://host.robots.ox.ac.uk/pascal/VOC/
http://gpcv.whu.edu.cn/data/Bijie_pages.html
Modeling Flood Risk with Machine Learning Algorithms and Python in GIS
Datasets and code for the Modeling Flood Risk with Machine Learning presented by Miss Mahdieh Shirmohammadi and team (Ph.D. Candidate).
For more information or downloading the datasets and code, click here.
ICWG III/IVa
