π― Model Builder yordamida NDVI xaritasi yaratildi!
π Hudud: Qashqadaryo viloyati, Gβuzor tumani
π Sana: 2025-yil 6-iyun
π°οΈ Sunβiy yoβldosh: Sentinel-2 (L2A)
Ushbu amaliy ishda asosiy maqsad β Model Builder vositasida takroriy bajariladigan geoprocessing jarayonlarini avtomatlashtirish, vaqt va resurslarni tejashga erishishdir.
π Ish bosqichlari:
1οΈβ£ 2 ta multi-spectral Sentinel-2 tasviri tanlab olindi β bu Gβuzor tumanini toβliq qamrab olish uchun zarur boβldi.
2οΈβ£ Raster Calculator orqali NDVI (Normalized Difference Vegetation Index) hisoblab chiqildi.
3οΈβ£ Mosaic jarayoni orqali ikki tasvir birlashtirildi.
4οΈβ£ Clip funksiyasi orqali Gβuzor tumani chegarasiga mos ravishda kesib olindi.
β Natijada, tuman hududidagi vegetatsiya holatini aks ettiruvchi aniq va vizual jihatdan qulay NDVI xaritasi yaratildi.
π Ushbu modelni boshqa tumanlar, sanalar yoki ArcGIS pro da o'zingiz kunlik bajaralidigan geoprocessing vazifalar uchun ham qoβllash mumkin.
π Hudud: Qashqadaryo viloyati, Gβuzor tumani
π Sana: 2025-yil 6-iyun
π°οΈ Sunβiy yoβldosh: Sentinel-2 (L2A)
Ushbu amaliy ishda asosiy maqsad β Model Builder vositasida takroriy bajariladigan geoprocessing jarayonlarini avtomatlashtirish, vaqt va resurslarni tejashga erishishdir.
π Ish bosqichlari:
1οΈβ£ 2 ta multi-spectral Sentinel-2 tasviri tanlab olindi β bu Gβuzor tumanini toβliq qamrab olish uchun zarur boβldi.
2οΈβ£ Raster Calculator orqali NDVI (Normalized Difference Vegetation Index) hisoblab chiqildi.
3οΈβ£ Mosaic jarayoni orqali ikki tasvir birlashtirildi.
4οΈβ£ Clip funksiyasi orqali Gβuzor tumani chegarasiga mos ravishda kesib olindi.
β Natijada, tuman hududidagi vegetatsiya holatini aks ettiruvchi aniq va vizual jihatdan qulay NDVI xaritasi yaratildi.
π Ushbu modelni boshqa tumanlar, sanalar yoki ArcGIS pro da o'zingiz kunlik bajaralidigan geoprocessing vazifalar uchun ham qoβllash mumkin.
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Machine Learning and Deep Learning
1οΈβ£ 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.
2οΈβ£Deep 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.
1οΈβ£ 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.
2οΈβ£Deep 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 QoΚ»chqorboyev (Blog)
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Forwarded from Qodirov_project
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β‘6π3π1
π°Remote 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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π°Remote 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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University`s Remote sensing centre
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GF2 satellites
1.Spatial resolution:
β¦ Panchromatic: 0.8 meters (This is its most prominent feature)
β¦ Multispectral: 3.2 meters
β Blue band: 0.45 - 0.52 ΞΌm
β Green band: 0.52 - 0.59 ΞΌm
β Red band: 0.63 - 0.69 ΞΌm
β Near Infrared band: 0.77 - 0.89 ΞΌm
2. Swath width:
β¦ Swath width of single - scene imaging: 23.5 kilometers (at the nominal orbital altitude)
Orbital parameters
3. Orbital type: Sun - synchronous return orbit
4. Orbital altitude: Approximately 631 kilometers
5. Inclination: Approximately 97.8Β°
6. Local time of descending node: Approximately 10:30 AM (to ensure good lighting conditions)
7. Revisit period:
β¦ Designed revisit period: 5 days (without side - looking)
β¦ Side - looking ability: It has a rapid side - looking ability (Β±35Β°). The revisit period can be shortened to 1 - 3 days through side - looking, which improves the observation flexibility.
1.Spatial resolution:
β¦ Panchromatic: 0.8 meters (This is its most prominent feature)
β¦ Multispectral: 3.2 meters
β Blue band: 0.45 - 0.52 ΞΌm
β Green band: 0.52 - 0.59 ΞΌm
β Red band: 0.63 - 0.69 ΞΌm
β Near Infrared band: 0.77 - 0.89 ΞΌm
2. Swath width:
β¦ Swath width of single - scene imaging: 23.5 kilometers (at the nominal orbital altitude)
Orbital parameters
3. Orbital type: Sun - synchronous return orbit
4. Orbital altitude: Approximately 631 kilometers
5. Inclination: Approximately 97.8Β°
6. Local time of descending node: Approximately 10:30 AM (to ensure good lighting conditions)
7. Revisit period:
β¦ Designed revisit period: 5 days (without side - looking)
β¦ Side - looking ability: It has a rapid side - looking ability (Β±35Β°). The revisit period can be shortened to 1 - 3 days through side - looking, which improves the observation flexibility.
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