Mahdi Khodadadzadeh

Objective

To develop a deep learning model to estimate the timing of phenological events over large area

To develop a model to estimate the timing of phenological events over large areas from time series of earth observation data using vision transformers, a fierce alternative to tra

Global production, transportation, and consumption of oil can cause inevitable spills into the environment (typically from pipelines, oil wells, and storage facilities) cont

The climate in cities and metropolitan areas (i.e., urban climate) is often different from the climate in the surrounding rural areas due to the presence of buildings, pavement, a

The Antarctic ice sheet covers almost the whole area of the Antarctic continent.

Deep Neural Networks (DNN) is considered as a panacea for different problems in several application areas. But what about spatial modeling?

Geo-referenced time series are data describing the time-changing behavior of one or more attributes at fixed locations and consistent time intervals [1].

Mapping the presence or abundance of an animal or plant species is a non-trivial challenge.  It often involves elaborate field surveys with strict data collection protocols and usually deep underst

Crop mapping plays an important role in agronomic planning and management both for farmers and policymakers. Satellite remote sensing sensors have become efficient tools for mapping croplands.

Impervious surfaces are the Earth’s surfaces covered with mainly anthropogenic and artificial structures, such as built-up areas, roads and streets, and parking lots.