Forwarded from Geoinfo.Uz π
π ArcGIS Pro bo'yicha videolar ro'yxati
1. ArcGIS Pro haqida
2. ArcGIS Pro dasturini o'rnatish
3. ArcGIS Pro Loyiha yaratish
4. Oddiy xarita tayyorlash
5. Qatlamlar bilan ishlash
6. O'zbekiston xaritasini tayyorlash
7. Samarqand viloyati xaritasini tayyorlash
8. ArcGIS Pro: Internetdan ma'lumot to'plash
ArcGIS Pro: LULC Xaritasini tayyorlash
ArcGIS Pro: Dem xaritasini tayyorlash (1-qism)
ArcGIS Pro: Dem xaritasini tayyorlash (2-qism)
Videodarslar davomi tezroq chiqishi uchun youtubedagi videoga izoh va layklar soni oshishi kerak
To'liq kurs haqida: https://t.me/arcgisprouz/88
Sayt | Telegram | Youtube | Kurslar | Fayllar
1. ArcGIS Pro haqida
2. ArcGIS Pro dasturini o'rnatish
3. ArcGIS Pro Loyiha yaratish
4. Oddiy xarita tayyorlash
5. Qatlamlar bilan ishlash
6. O'zbekiston xaritasini tayyorlash
7. Samarqand viloyati xaritasini tayyorlash
8. ArcGIS Pro: Internetdan ma'lumot to'plash
ArcGIS Pro: LULC Xaritasini tayyorlash
ArcGIS Pro: Dem xaritasini tayyorlash (1-qism)
ArcGIS Pro: Dem xaritasini tayyorlash (2-qism)
Videodarslar davomi tezroq chiqishi uchun youtubedagi videoga izoh va layklar soni oshishi kerak
To'liq kurs haqida: https://t.me/arcgisprouz/88
Sayt | Telegram | Youtube | Kurslar | Fayllar
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π°Remote Sensing Innovators
Web Map
Assalomu alaykum, Agar biror bir insonga shu xarita yoqqan bo`lsa commentlarga yozib qoldiriylar birgalikda tayyorlaymiz, balki boshqa mavzuda ham tayyorlab ko`rarmiz, agar qiziqish bo`lsa
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Forwarded from π°Remote Sensing Innovators (ADπ°οΈ)
πΎ Qashqadaryo viloyati Qarshi tumanida joylashgan qishloq xo'jaligi maydonlari vegetatsiya tahlili (Study area uchun tanlangan maydonlar) π
Quyidagi suratda Qarshi tumani yaqinidagi qishloq xo'jaligi yerlarida vegetatsiya tahlili o'tkazildi. Tahlil Google Earth Engine dasturidan foydalangan holda, 2023-2024 yillar uchun amalga oshirildi. Xarita turli vegetatsiya darajalarini ko'rsatadi:
πΎWeb Xaritani ko'rish
π΄ Qizil: Suv yoki boshqa obyektlar
βͺοΈ Kulrang: Bo'sh maydon
π‘ Sariq: Kam vegetatsiya
π’ Och yashil: O'rtacha vegetatsiya
π© Yashil: Yaxshi vegetatsiya
Ushbu tahlil o'simliklar holatini tushunish va qishloq xo'jaligini boshqarishda resurslarni samarali taqsimlash uchun juda muhimdir.
π±Telegram_channel
Mualiif : @Davlatov_9959
Quyidagi suratda Qarshi tumani yaqinidagi qishloq xo'jaligi yerlarida vegetatsiya tahlili o'tkazildi. Tahlil Google Earth Engine dasturidan foydalangan holda, 2023-2024 yillar uchun amalga oshirildi. Xarita turli vegetatsiya darajalarini ko'rsatadi:
πΎWeb Xaritani ko'rish
π΄ Qizil: Suv yoki boshqa obyektlar
βͺοΈ Kulrang: Bo'sh maydon
π‘ Sariq: Kam vegetatsiya
π’ Och yashil: O'rtacha vegetatsiya
π© Yashil: Yaxshi vegetatsiya
Ushbu tahlil o'simliklar holatini tushunish va qishloq xo'jaligini boshqarishda resurslarni samarali taqsimlash uchun juda muhimdir.
π±Telegram_channel
Mualiif : @Davlatov_9959
π₯4π4π1
Forwarded from GISPad | SARDORBEK MAVLONBEKOV
South GNSS G9 haqida video qo'llanma!
Fikringizni izohlarda qoldiring bu biz uchun muhim!
Do'stlarga ulashing π@GISPad
https://youtu.be/YifIUJbBSB4?si=QtcaYXx_JQ7nTFE1
Fikringizni izohlarda qoldiring bu biz uchun muhim!
Do'stlarga ulashing π@GISPad
https://youtu.be/YifIUJbBSB4?si=QtcaYXx_JQ7nTFE1
YouTube
GNSS South G9 | #GISPad
O'zbek tilidagi darsliklar to'plamidan, video yoqgan bo'lsa like bosib izohlarda o'z fikringizni qoldiring.
Sizning fikringiz biz uchun muhim!
Bizni loyihani qo'llab quvvatlang
(5555 3666 0268 6050)
#GISPad
Sizning fikringiz biz uchun muhim!
Bizni loyihani qo'llab quvvatlang
(5555 3666 0268 6050)
#GISPad
π7π₯1π―1
πΊ 2019β2024 yillarda o`ralig`ida har fasldagi vegetatsiya oβzgarishlarini turli xil indexlar orqali tahliliπΊ
πΊ Analysis of Seasonal Vegetation Changes from 2019 to 2024 Using Various Indices πΊ
πΊWeb mapπΎ
πΊ Analysis of Seasonal Vegetation Changes from 2019 to 2024 Using Various Indices πΊ
πΊWeb mapπΎ
π8π€2π1π―1
#Savol javoblar : o'zingizni qiziqtirgan savollarni yozib qoldiring?
π1π₯1π―1
πΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏπΊπΏBiz Jahon Chempionatidamiz!
Oβzbekiston xalqi, Oβzbekiston davlatining tarixiy gβalabasi barchamizga muborak boβlsin!
Oβzbekiston xalqi, Oβzbekiston davlatining tarixiy gβalabasi barchamizga muborak boβlsin!
π7β€2π₯1
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.
β‘5β€2π―1
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)
π3
Forwarded from Qodirov_project
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