scholarly journals A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

2018 ◽  
Vol 144 ◽  
pp. 325-340 ◽  
Author(s):  
Pardhasaradhi Teluguntla ◽  
Prasad S Thenkabail ◽  
Adam Oliphant ◽  
Jun Xiong ◽  
Murali Krishna Gumma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Random Forest ◽  
Learning Algorithm ◽  
Google Earth ◽  
Machine Learning Algorithm ◽  
Computing Platform ◽  
Cloud Computing Platform ◽  
Google Earth Engine

scholarly journals Land Cover Classification Based on Cloud Computing Platform

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Nghia Viet Nguyen ◽  
Thu Hoai Thi Trinh ◽  
Hoa Thi Pham ◽  
Trang Thu Thi Tran ◽  
Lan Thi Pham ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Spatial Data ◽  
Google Earth ◽  
Computer Hardware ◽  
Commercial Software ◽  
Computing Platform ◽  
Land Cover Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.

scholarly journals WATER QUALITY MONITORING OVER FINGER LAKES REGION USING SENTINEL-2 IMAGERY ON GOOGLE EARTH ENGINE CLOUD COMPUTING PLATFORM

2021 ◽  
Vol V-3-2021 ◽  
pp. 279-283
Author(s):  
R. M. Khan ◽  
B. Salehi ◽  
M. Mahdianpari ◽  
F. Mohammadimanesh
Keyword(s):  
Water Quality ◽  
Cloud Computing ◽  
Agricultural Runoff ◽  
Google Earth ◽  
Computing Platform ◽  
Finger Lakes ◽  
Cloud Computing Platform ◽  
Google Earth Engine ◽  
Sentinel 2 ◽  
Finger Lakes Region

Abstract. Surface water quality is degrading continuously both due to natural and anthropogenic causes. There are several indicators of water quality, among which sediment loading is mainly determined by turbidity. Normalized Difference Water Index (NDWI) is one indirect measure of sediments present in water. This study focuses on detecting and monitoring sediments through NDWI over the Finger Lakes region, New York. Time series analysis is performed using Sentinel 2 imagery on the Google Earth Engine (GEE) platform. Finger Lakes region holds high socio-economic value because of tourism, water-based recreation, industry, and agriculture sector. The deteriorating water quality within the Finger Lake region has been reported based on ground sampling techniques. This study takes advantage of a cloud computing platform and medium resolution atmospherically corrected satellite imagery to detect and analyse water quality through sediment detection. In addition, precipitation data is used to understand the underlying cause of sediment increase. The results demonstrate the amount of sediments is greater in the early spring and summer months compared to other seasons. This can be due to the agricultural runoff from the nearing areas as a result of high precipitation. The results confirm the necessity for monitoring the quality of these lakes and understanding the underlying causes, which are beneficial for all the stakeholders to devise appropriate policies and strategies for timely preservation of the water quality.

scholarly journals Google Earth Engine Applications

2019 ◽  
Vol 11 (5) ◽  
pp. 591 ◽  
Author(s):  
Onisimo Mutanga ◽  
Lalit Kumar
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Google Earth ◽  
Data Sets ◽  
Computing Platform ◽  
Huge Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

The Google Earth Engine (GEE) is a cloud computing platform designed to store and process huge data sets (at petabyte-scale) for analysis and ultimate decision making [...]

GOOGLE EARTH ENGINE AS CLOUD COMPUTING PLATFORM IN REMOTE SENSING POSTGRADUATE STUDIES

2020 ◽  
Author(s):  
Manuel Campos-Taberner ◽  
Francisco Javier García-Haro ◽  
Beatriz Martínez ◽  
Sergio Sánchez-Ruiz ◽  
María Amparo Gilabert
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Google Earth ◽  
Computing Platform ◽  
Postgraduate Studies ◽  
Cloud Computing Platform ◽  
Google Earth Engine

scholarly journals Pemetaan Potensi Lahan Jagung Menggunakan Citra Satelit Dan Random Forest Pada Cloud computing Google Earth Engine

2021 ◽  
Vol 2021 (1) ◽  
pp. 1001-1011
Author(s):  
Dwi Wahyu Triscowati ◽  
Widyo Pura Buana ◽  
Arif Handoyo Marsuhandi
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Random Forest ◽  
Input Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Google Earth Engine ◽  
Sentinel 2

Ketersediaan informasi potensi lahan jagung yang cepat terbaharui penting untuk mendukung pemulihan ekonomi pasca covid 19. Pemetaan jagung menjadi suatu tantangan tersendiri di bidang pertanian karena areal penanaman jagung tidak memiliki ciri khusus seperti sawah, jagung belum memiliki peta luas baku, serta  penanamannya dapat dilakukan di sawah maupun lahan-lahan kering hutan. Permasalahan lainnya, perlu sumberdaya komputasi yang tinggi jika pemetaan jagung dilakukan secara langsung ataupun identifikasi secara manual. Dalam penelitian ini dilakukan pemetaan potensi jagung di Jawa Timur pada Kabupaten terpilih secara otomatis menggunakan Machine learning pada cloud computing google earth engine. Dengan penggunaan cloud computing GEE, pemetaan jagung dapat dilakukan pada area luas tanpa terkendala kemampuan komputer. Penelitian ini menggunakan algoritma pembelajaran mesin Random Forest(RF) berbasis piksel dengan input data dari satelit Landsat-8, Sentinel-1 dan Sentinel-2. Data referensi untuk melatih model klasifikasi menggunakan hasil KSA jagung. Akurasi hasil Machine learning paling baik berasal dari kombinasi Landsat-8 dan Sentinel-2 dengan rataan akurasi sebesar 0.79. Model klasifikasi kemudian diaplikasikan pada 10 Kabupaten dimana hasil terbaik adalah pada Kabupaten Banyuwangi dengan akurasi  0.89. Dilihat dari luas potensi jagung pada daerah Banyuwangi luasan berkisar dari 22.256,82 – 58.992,3 Ha berdasarkan pixel yang terprediksi sebagai jagung minimal 3 kali/bulan. Dari hasil kajian ini terbukti bahwa penggunaan cloud computing dapat melakukan penghitungan pada 10 Kabupaten secara cepat baik dari sisi pembangunan model maupun dari prediksinya. Selain itu karena menggunakan cloud computing pemanfaatan citra satelit dapat dimanfaatkan secepat mungking setelah citra satelit terbit/rilis sehingga prediksi dari potensi jagung dapat secara cepat dan tepat dihasilkan. Kajian ini juga menyoroti kekurangan yang terjadi yaitu dari sisi jumlah sampel untuk data latih dan keterbatasan algoritma yang digunakan sehingga kedepannya dapat dikembangkan lebih baik lagi.

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