
Mazkur tadqiqotning asosiy maqsadi masofadan zondlash (Remote Sensing) va sun’iy intellekt (AI) texnologiyalari yordamida noqonuniy daraxt kesish holatlarini aniqlash hamda monitoring qilish tizimini ishlab chiqish va tahlil etishdan iborat. An’anaviy nazorat usullari keng hududlarni qamrab olishda yetarli samaradorlikka ega emas. Shu sababli sun’iy yo‘ldosh tasvirlari ekologik monitoring jarayonida muhim vosita sifatida namoyon bo‘lmoqda. Tadqiqotda Sentinel-2 va Landsat ma’lumotlari asosida NDVI (Normallashgan farqli vegetatsiya indeksi) hisoblash hamda Change Detection algoritmlari orqali o‘zgarishlarni aniqlash usullari ko‘rib chiqildi. Shuningdek, CNN, U-Net va DeepLabV3+ kabi chuqur o‘rganish modellarining samaradorligi tahlil qilindi. Tizimning amaliy ahamiyati ekologik inspeksiya xodimlariga real vaqt rejimida shubhali hududlar bo‘yicha ogohlantirish yuborish va huquqbuzarliklarni masofadan turib aniqlash imkonini yaratishidadir. Tadqiqot natijalari tizimning yuqori aniqlik darajasiga ega ekanligini hamda o‘rmon qoplamasini saqlashda samarali vosita bo‘lishi mumkinligini ko‘rsatdi.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
