๐บ 2019โ2024 yillarda o`ralig`ida har fasldagi vegetatsiya oโzgarishlarini turli xil indexlar orqali tahlili๐บ
๐บ Analysis of Seasonal Vegetation Changes from 2019 to 2024 Using Various Indices ๐บ
๐บWeb map๐พ
๐บ Analysis of Seasonal Vegetation Changes from 2019 to 2024 Using Various Indices ๐บ
๐บWeb map๐พ
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#Savol javoblar : o'zingizni qiziqtirgan savollarni yozib qoldiring?
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๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟ๐บ๐ฟBiz Jahon Chempionatidamiz!
Oโzbekiston xalqi, Oโzbekiston davlatining tarixiy gโalabasi barchamizga muborak boโlsin!
Oโzbekiston xalqi, Oโzbekiston davlatining tarixiy gโalabasi barchamizga muborak boโlsin!
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๐ฏ Model Builder yordamida NDVI xaritasi yaratildi!
๐ Hudud: Qashqadaryo viloyati, Gโuzor tumani
๐ Sana: 2025-yil 6-iyun
๐ฐ๏ธ Sunโiy yoโldosh: Sentinel-2 (L2A)
Ushbu amaliy ishda asosiy maqsad โ Model Builder vositasida takroriy bajariladigan geoprocessing jarayonlarini avtomatlashtirish, vaqt va resurslarni tejashga erishishdir.
๐ Ish bosqichlari:
1๏ธโฃ 2 ta multi-spectral Sentinel-2 tasviri tanlab olindi โ bu Gโuzor tumanini toโliq qamrab olish uchun zarur boโldi.
2๏ธโฃ Raster Calculator orqali NDVI (Normalized Difference Vegetation Index) hisoblab chiqildi.
3๏ธโฃ Mosaic jarayoni orqali ikki tasvir birlashtirildi.
4๏ธโฃ Clip funksiyasi orqali Gโuzor tumani chegarasiga mos ravishda kesib olindi.
โ Natijada, tuman hududidagi vegetatsiya holatini aks ettiruvchi aniq va vizual jihatdan qulay NDVI xaritasi yaratildi.
๐ Ushbu modelni boshqa tumanlar, sanalar yoki ArcGIS pro da o'zingiz kunlik bajaralidigan geoprocessing vazifalar uchun ham qoโllash mumkin.
๐ Hudud: Qashqadaryo viloyati, Gโuzor tumani
๐ Sana: 2025-yil 6-iyun
๐ฐ๏ธ Sunโiy yoโldosh: Sentinel-2 (L2A)
Ushbu amaliy ishda asosiy maqsad โ Model Builder vositasida takroriy bajariladigan geoprocessing jarayonlarini avtomatlashtirish, vaqt va resurslarni tejashga erishishdir.
๐ Ish bosqichlari:
1๏ธโฃ 2 ta multi-spectral Sentinel-2 tasviri tanlab olindi โ bu Gโuzor tumanini toโliq qamrab olish uchun zarur boโldi.
2๏ธโฃ Raster Calculator orqali NDVI (Normalized Difference Vegetation Index) hisoblab chiqildi.
3๏ธโฃ Mosaic jarayoni orqali ikki tasvir birlashtirildi.
4๏ธโฃ Clip funksiyasi orqali Gโuzor tumani chegarasiga mos ravishda kesib olindi.
โ Natijada, tuman hududidagi vegetatsiya holatini aks ettiruvchi aniq va vizual jihatdan qulay NDVI xaritasi yaratildi.
๐ Ushbu modelni boshqa tumanlar, sanalar yoki ArcGIS pro da o'zingiz kunlik bajaralidigan geoprocessing vazifalar uchun ham qoโllash mumkin.
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Machine Learning and Deep Learning
1๏ธโฃ Machine Learning (ML) involves algorithms and models that can learn from and make predictions or decisions based on data. Machine learning algorithms are designed to learn patterns from data and make predictions or decisions without explicit programming.
2๏ธโฃDeep Learning (DL) is a subset of machine learning that uses neural networks with multiple layers to learn from data. Deep learning algorithms, inspired by the structure of the human brain, consist of artificial neural networks with multiple layers (hence the term "deep"). These networks are capable of learning representations of data with multiple levels of abstraction. Deep learning excels in handling large volumes of data and can automatically discover patterns within it.
1๏ธโฃ Machine Learning (ML) involves algorithms and models that can learn from and make predictions or decisions based on data. Machine learning algorithms are designed to learn patterns from data and make predictions or decisions without explicit programming.
2๏ธโฃDeep Learning (DL) is a subset of machine learning that uses neural networks with multiple layers to learn from data. Deep learning algorithms, inspired by the structure of the human brain, consist of artificial neural networks with multiple layers (hence the term "deep"). These networks are capable of learning representations of data with multiple levels of abstraction. Deep learning excels in handling large volumes of data and can automatically discover patterns within it.
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Forwarded from Azizbek Qoสปchqorboyev (Blog)
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Forwarded from Qodirov_project
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๐ฐRemote Sensing Innovators
https://www.linkedin.com/posts/thinkgeoedu_geospatial-data-remotesensing-ugcPost-7339639841782984707-pudB?utm_source=share&utm_medium=member_desktop&rcm=ACoAAEF2uRMBs4n1zra-au2CwtHhgYuGw3x0p-o
Synthetic Aperture Radar (SAR) is a revolutionary technology in the field of remote sensing, enabling data collection regardless of weather conditions or daylight
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๐ฐRemote Sensing Innovators
#The Top university ranking Remote Sensing
After the summer school, I will apply the knowledge and experience I gained here to contribute to the development of my field.
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