Sajad Sayadi

The 8th ISPRS Geospatial Conference 2025 at the University of Tehran, Iran, December 16, 2025. A research poster titled "Detection and Attribution of Thermal Anomalies in Urban Blocks of Qom Using Time-Series Analysis of TIR Imagery". The visual content includes spatial distribution maps of 22 identified thermal anomalies and LST time-series graphs. The methodology highlights Median Absolute Deviation (MAD) for outlier removal and noise filtering and the Reed-Xiaoli (RX) algorithm for anomaly detection and identifying deviated thermal behaviors.

Stepping out of the forest for a day to apply my Remote Sensing expertise to the urban landscape!

Presented my poster on detecting thermal anomalies using Time-Series analysis at the 8th ISPRS Geospatial Conference 2025.

#RemoteSensing #ISPRS #TimeSeries #LST #ISPRSConference

🌐🌍🛰️🍁🌲🔥

An oak leaf floating on water

A very fascinating and detailed study on estimating Site Index using mobile laser scanning and deep learning, along with an excellent discussion on the challenges ahead. 🍁 🌳🔥 🌐

#ForestEcology
Thanks to @mariamoan.bsky.social and @hansoleorka.bsky.social and co-authors.
@skogrover.bsky.social

Comparison of Global Shutter (full sensor exposure) vs. Rolling Shutter (sequential line-by-line exposure).

#Photogrammetry #Imaging #Shutter #UAVphotogrammetry
#3DModeling #RemoteSensing #CameraTech

No matter what camera you use to capture images, you need to understand how rolling shutter and global shutter work and what sets them apart.

#Photogrammetry #Imaging #Shutter #UAVphotogrammetry
#3DModeling #RemoteSensing #CameraTech

Flowchart for moving beyond NDVI: Sentinel-2 multispectral data feeds two paths: (a) Generation of NDVI; (b) Calculate EVI coefficients with PSO → Classification with EVI. In parallel, Google Earth imagery → Manual classification (veg/non-veg).

Looking for something better than NDVI as a vegetation index?
Follow the flowchart below.

#RemoteSensing #Vegetationindex #NDVI #EVI #Sentinel2 #Workflow

See more details here👉 doi.org/10.1109/Metr...

Fantastic work and what a view! It's always great to see meteorological data collection in action. This is precisely the kind of valuable data that helps us understand, analyze, and act on weather conditions.

I'm glad you found the research interesting. I'd love to hear your thoughts, or the thoughts of others in this field. Do you have any experience with scene-specific tuning of EVI parameters?

Red: vegetation by tuned EVI but non-vegetation by traditional EVI; Blue: the opposite

We conducted a binary visual comparison of EVI vegetation maps (traditional vs optimized) over a semi-arid mountain site.

#RemoteSensing #EVI #SemiArid #VegetationIndex #Optimization #Binarymap

See more details here👇 doi.org/10.1109/Metr...

NDVI (left) vs PSO-tuned EVI (right) in semi-arid mountains; the optimized EVI produced a wider vegetation value range, higher variance, and mapped more vegetation pixels than NDVI and the conventional EVI.

‪Sajad Sayadi‬
Classic NDVI can be fuzzy over bright soils and sparse canopies. We re-parameterized EVI with PSO for semi-arid mountains on Sentinel-2.
Have you tried scene-specific EVI tuning?

#RemoteSensing #EVI #Vegetation #Indexes #PSO #Sentinel2

See more details here👇 doi.org/10.1109/Metr...