Muhammed Oğuzhan Mete

Assoc. Prof. @itu1773 | PhD. Geomatics Engineering | AWS Community Builder @awscloud | Land Management, Real Estate Valuation | GIS | ML/DL | Cloud | Big Data

[Muhammed Oğuzhan Mete @metemu]

Our new paper titled "Disaster management with cloud-based geographic information systems: site selection of landfill areas after Kahramanmaraş, Türkiye earthquake sequence" has been published in "Environmental Earth Science”.

@SpringerNature

🌐
https://t.co/SmuLs5C5VI

[Muhammed Oğuzhan Mete @metemu]

#disastermanagement #GIS #cloud #multicriteria #bestworstmethod #landfill #siteselection #gischat

[Muhammed Oğuzhan Mete @metemu]

Our new paper titled "Disaster management with cloud-based geographic information systems: site selection of landfill areas after Kahramanmaraş, Türkiye earthquake sequence" has been published in "Environmental Earth Science”.

@SpringerNature

🌐
https://t.co/SmuLs5C5VI

[Muhammed Oğuzhan Mete @metemu]

Unlike existing studies, this study provides significant improvements in disaster management processes in terms of speed, cost, and security with a cloud-GIS-supported data management approach.

[Muhammed Oğuzhan Mete @metemu]

Destekleriyle lisans öğrencilerinin araştırma ekosistemine dahil olmasını sağlayan TÜBİTAK’a teşekkür ederiz. #itu #geomatik #cbs #gis #makineogrenmesi #elektrikliArac #sarjistasyonu

@itu1773
@ProfDrKoyuncu
@Tubitak
@ProfHasanMandal

[Muhammed Oğuzhan Mete @metemu]

TÜBİTAK 2209-A kapsamında, danışmanlığını yaptığım "CBS ve Makine Öğrenmesi Yöntemleri İle Elektrikli Araç Şarj İstasyonları Yer Seçimi Modelinin Geliştirilmesi" başlıklı proje desteğe değer görüldü. Proje ekibinde yer alan öğrencilerim Hilal Erçin ve Engin Özcan’ı tebrik ederim.

[İTÜ Harita Mühendisliği @itu_geomatik]

TÜBİTAK 2209-A çağrısı sonuçlarına göre bölümümüz lisans öğrencilerinin 3 projesi desteklenmeye değer görüldü.
Projelerde yer alan bütün öğrencilerimizi ve danışmanlık yapan akademisyenlerimizi kutluyor, başarılarının devamını diliyoruz.

[Wherobots @wherobots]

This month's @wherobots & @ApacheSedona featured community member is @metemu. Read about his presentation at @foss4g on "Geospatial Big Data Analytics For Sustainable Smart Cities" & other exciting news in the latest edition of "This Month In Wherobots":

https://t.co/DQRvjQuBKQ

[Muhammed Oğuzhan Mete @metemu]

Published paper:
Mete, M. O.: GEOSPATIAL BIG DATA ANALYTICS FOR SUSTAINABLE SMART CITIES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W7-2023, 141–146, https://t.co/xmQMJCvpCp, 2023.

[Muhammed Oğuzhan Mete @metemu]

I've presented my latest work titled "GEOSPATIAL BIG DATA ANALYTICS FOR SUSTAINABLE SMART CITIES" at #FOSS4G2023 in Prizren, Kosovo. The slides are available in https://t.co/RzHW7iFn97. Thanks to everyone who attended and contributed. #GIS #Geospatial #SmartCity #SmartEnvironment

[Muhammed Oğuzhan Mete @metemu]

I will attend FOSS4G 2023 to give an oral presentation about my latest work titled "Geospatial Big Data Analytics for Sustainable Smart Cities" in June. See you in Prizren, Kosovo.

https://t.co/a0YAqCY5Of

#openscience #foss4g #GIS #geospatial #bigdata #smartcity

[Kurt Menke - @geomenke.bsky.social @geomenke]

I am so happy to see all the raster attribute table (RAT) features in #QGIS 3.30 's-Hertogenbosch! Thanks @elpaso66

[Said Turksever @asturksever]

Thank you all @openstreetmap community for lighting up the map of affected area during these hard times.

@YerCizenler @OpenstreetmapTr @hotosm
#TurkeySyriaEarthquake2023

[Cloud Türkiye @CloudTurkiye]

📢 The call for papers for #AWS Community Day Turkey 2023 is now open!

We invite you to share your knowledge with 400 Cloud experts in İstanbul! The deadline is Feb 19, 2023.

(Konuşmalar Türkçe veya İngilizce olabilir. Detaylar linkte 👇)
https://t.co/yEAryFMR61

[Muhammed Oğuzhan Mete @metemu]

@mustuner2 Many thanks @mustuner2 hocam. All the best!

[Muhammed Oğuzhan Mete @metemu]

Adding locational factors to the property price data and applying a novel nominally weighted spatial clustering algorithm for creating a local regression increased the prediction accuracy by about 45%. It also demonstrated that Random Forest was the most accurate ensemble model.

[Muhammed Oğuzhan Mete @metemu]

Our new paper with Prof. Dr. @tyomralioglu, "A Hybrid Approach for Mass Valuation of Residential Properties through Geographic Information Systems and Machine Learning Integration" has just been published in @GeogAnalysis

https://t.co/naFPiKIf3w

👇 https://t.co/5tCvTU8vyw

[Muhammed Oğuzhan Mete @metemu]

Local SHapley Additive exPlanations (SHAP) values increase model explainability and give more detailed information about the influencing factors in different value zones.