Salim SoltaniUniversity of Freiburg | Albert-Ludwigs-Universität Freiburg · Faculty of Environment and Natural Resources
Salim Soltani
Master of Science
PhD student at the Sensor-based Geoinformatics (GeoSense), Uni-Freiburg.Focus: remote sensing,CV, machine learning
About
8
Publications
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Introduction
Ph.D. candidate in the field of Machine Learning and Data Science at the University of Leipzig.
Skills and Expertise
Publications
Publications (8)
Climate change is negatively impacting the world's biodiversity. To build automated systems to monitor these negative biodiversity impacts, large-scale, volunteer-collected datasets like iNaturalist are built from community-identified, natural imagery. However, such volunteer-based data are opportunistic and lack a structured sampling strategy, res...
Knowledge of plant species distributions is essential for various application fields, such as nature conservation, agriculture, and forestry. Remote sensing data, especially high-resolution orthoimages from unoccupied aerial vehicles (UAVs), paired with novel pattern-recognition methods, such as convolutional neural networks (CNNs), enable accurate...
Knowledge of plant species distributions is essential for various applications, such as nature conservation, agriculture, and forestry. Remote sensing data, especially high-resolution orthoimages from Unoccupied Aerial Vehicles (UAVs), were demonstrated to be an effective data source for plant species mapping. Particularly, in concert with novel pa...
Accurate information on the spatial distribution of plant species and communities is in high demand for various fields of application, such as nature conservation, forestry, and agriculture. A series of studies has shown that Convolutional Neural Networks (CNNs) accurately predict plant species and communities in high-resolution remote sensing data...
The Kunduz River is one of the main tributaries of the Amu Darya Basin in North Afghanistan. Many communities live in the Kunduz River Basin (KRB), and its water resources have been the basis of their livelihoods for many generations. This study investigates climate change impacts on the KRB catchment. Rare station data are, for the first time, use...
The world population is growing, and a majority of the population is and will be living in urban areas. Nearly 90 percent of this growth takes place in Asia and Africa. However, urbanisation processes are not distributed evenly. Mostly they are concentrated in prosperous regions, at infrastructural nodes, or along trade routes. The so-called New Si...
The Kunduz River is one of the main tributaries of the Amu Darya Basin in North Afghanistan. Many communities live in the Kunduz River Basin (KRB), and its water resources have been the basis of their livelihoods for many generations. This study investigates climate change impacts on the KRB catchment. Rare station data are, for the first time, use...
the total global population of this big cat species is estimated to be around 4080-6590. They spread across an area of 2 million square km with most of them being found in China, followed by Mongolia and India. The animal favors a rugged terrain interspersed with steep slopes, ridges, gullies, rocky outcrops, mostly an elevation of of 3000-4500m m....
Questions
Question (1)
Dear all,
I am trying to classify urban areas using Landsat TM data, I have tried to get the image texture using GLCM and the result is somehow good, but I have seen many other literatures which exploited Multivariate Image texture and had better result compare to GLCM method.
GLCM: uses one band as input data
Multivariate Img text: uses multi-band as input data
here is a paper regarding Multivariate Img Text : https://pdfs.semanticscholar.org/e5b4/9480f331eaf33de24f48b2304060438f2333.pdf
I appreciate your help.
regards,
Salim