Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

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Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

Remote Sensing ◽

10.3390/rs13010010 ◽

2020 ◽

Vol 13 (1) ◽

pp. 10

Author(s):

Andrea Sulova ◽

Jamal Jokar Arsanjani

Keyword(s):

Climate Change ◽

Machine Learning ◽

Random Forest ◽

Google Earth ◽

Summer Season ◽

Driving Factors ◽

Machine Learning Algorithms ◽

Classification And Regression Tree ◽

Training Dataset ◽

Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

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Machine Learning Comparison and Parameter Setting Methods for the Detection of Dump Sites for Construction and Demolition Waste Using the Google Earth Engine

Remote Sensing ◽

10.3390/rs13040787 ◽

2021 ◽

Vol 13 (4) ◽

pp. 787

Author(s):

Lei Zhou ◽

Ting Luo ◽

Mingyi Du ◽

Qiang Chen ◽

Yang Liu ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Google Earth ◽

Construction And Demolition Waste ◽

Parameterization Scheme ◽

Classification Methods ◽

Demolition Waste ◽

Optimal Method ◽

Identification Method ◽

Google Earth Engine

Machine learning has been successfully used for object recognition within images. Due to the complexity of the spectrum and texture of construction and demolition waste (C&DW), it is difficult to construct an automatic identification method for C&DW based on machine learning and remote sensing data sources. Machine learning includes many types of algorithms; however, different algorithms and parameters have different identification effects on C&DW. Exploring the optimal method for automatic remote sensing identification of C&DW is an important approach for the intelligent supervision of C&DW. This study investigates the megacity of Beijing, which is facing high risk of C&DW pollution. To improve the classification accuracy of C&DW, buildings, vegetation, water, and crops were selected as comparative training samples based on the Google Earth Engine (GEE), and Sentinel-2 was used as the data source. Three classification methods of typical machine learning algorithms (classification and regression trees (CART), random forest (RF), and support vector machine (SVM)) were selected to classify the C&DW from remote sensing images. Using empirical methods, the experimental trial method, and the grid search method, the optimal parameterization scheme of the three classification methods was studied to determine the optimal method of remote sensing identification of C&DW based on machine learning. Through accuracy evaluation and ground verification, the overall recognition accuracies of CART, RF, and SVM for C&DW were 73.12%, 98.05%, and 85.62%, respectively, under the optimal parameterization scheme determined in this study. Among these algorithms, RF was a better C&DW identification method than were CART and SVM when the number of decision trees was 50. This study explores the robust machine learning method for automatic remote sensing identification of C&DW and provides a scientific basis for intelligent supervision and resource utilization of C&DW.

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Machine Learning as a Silent Observer of Advanced Geoscience Interpretation

10.2118/207939-ms ◽

2021 ◽

Author(s):

Muhammad Sajid

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Transfer Learning ◽

Learning Performance ◽

Learning Approach ◽

Seismic Fault ◽

Facies Classification ◽

The World ◽

Learning Machine ◽

Fault Interpretation

Abstract Machine learning is proving its successes in all fields of life including medical, automotive, planning, engineering, etc. In the world of geoscience, ML showed impressive results in seismic fault interpretation, advance seismic attributes analysis, facies classification, and geobodies extraction such as channels, carbonates, and salt, etc. One of the challenges faced in geoscience is the availability of label data which is one of the most time-consuming requirements in supervised deep learning. In this paper, an advanced learning approach is proposed for geoscience where the machine observes the seismic interpretation activities and learns simultaneously as the interpretation progresses. Initial testing showed that through the proposed method along with transfer learning, machine learning performance is highly effective, and the machine accurately predicts features requiring minor post prediction filtering to be accepted as the optimal interpretation.

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PENERAPAN MACHINE LEARNING BERBASIS DATA GEOSPASIAL UNTUK OPTIMALISASI LAHAN PERTANIAN PADA MASA PANDEMI DAN PASCA PANDEMI

Seminar Nasional Geomatika ◽

10.24895/sng.2020.0-0.1131 ◽

2021 ◽

pp. 161

Author(s):

Royyannuur Kurniawan Endrayanto ◽

Adharul Muttaqin

Keyword(s):

Machine Learning ◽

Random Forest ◽

Early Warning Systems ◽

Google Earth ◽

Warning Systems ◽

Land Data Assimilation ◽

Google Earth Engine ◽

Land Data Assimilation System ◽

Data Assimilation System ◽

Assimilation System

Pertanian merupakan salah satu sektor penting karena dapat memenuhi kebutuhan pangan sebagai kebutuhan pokok. Kebutuhan pangan masih menjadi salah satu isu hangat terlebih di masa pandemi COVID- 19 seperti saat ini. Pemenuhan kebutuhan pangan juga berkaitan erat dengan jumlah bahan pangan yang diproduksi oleh petani. Lingkungan merupakan salah satu faktor keberhasilan dalam kegiatan pertanian. Kondisi lingkungan Indonesia yang beragam seperti suhu dan tingkat presipitasi menyebabkan adanya perbedaan jenis tanaman pangan potensial setiap daerah di Indonesia. Oleh karena itu perlu upaya untuk mengoptimalkan produksi lahan pertanian berdasarkan faktor lingkungan di setiap daerah. Upaya ini diharapkan dapat membantu menjaga ketahanan pangan baik di masa pandemi dan pasca pandemi. Pada penelitian ini diperkenalkan pemanfaatan data geospasial untuk klasifikasi jenis tanaman pangan menggunakan algoritma machine learning sebagai upaya optimalisasi lahan pertanian. Data yang digunakan adalah Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). Algoritma machine learning yang digunakan adalah algoritma klasifikasi Random Forest. Teknologi yang digunakan adalah Google Colab, Google Earth Engine dan Python. Tujuan dari penelitian ini adalah untuk mengklasifikasikan tanaman pangan yang memiliki potensi paling baik untuk ditanam di suatu daerah berdasarkan kondisi lingkungan yang ada.

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A preliminary study on machine learning and google earth engine for mangrove mapping

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/500/1/012038 ◽

2020 ◽

Vol 500 ◽

pp. 012038

Author(s):

Muhammad Kamal ◽

Nur Mohammad Farda ◽

Ilham Jamaluddin ◽

Artha Parela ◽

Ketut Wikantika ◽

...

Keyword(s):

Machine Learning ◽

Google Earth ◽

Preliminary Study ◽

Google Earth Engine

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An Urban Water Extraction Method Combining Deep Learning and Google Earth Engine

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2020.2971783 ◽

2020 ◽

Vol 13 ◽

pp. 769-782 ◽

Cited By ~ 6

Author(s):

Yudie Wang ◽

Zhiwei Li ◽

Chao Zeng ◽

Gui-Song Xia ◽

Huanfeng Shen

Keyword(s):

Deep Learning ◽

Extraction Method ◽

Google Earth ◽

Water Extraction ◽

Urban Water ◽

Google Earth Engine

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Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms

Remote Sensing ◽

10.3390/rs12223776 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3776

Author(s):

Andrea Tassi ◽

Marco Vizzari

Keyword(s):

Machine Learning ◽

Central Italy ◽

Object Oriented ◽

Google Earth ◽

Machine Learning Algorithms ◽

Support Vector ◽

Landsat 8 ◽

Good Reliability ◽

Google Earth Engine ◽

Occurrence Matrix

Google Earth Engine (GEE) is a versatile cloud platform in which pixel-based (PB) and object-oriented (OO) Land Use–Land Cover (LULC) classification approaches can be implemented, thanks to the availability of the many state-of-art functions comprising various Machine Learning (ML) algorithms. OO approaches, including both object segmentation and object textural analysis, are still not common in the GEE environment, probably due to the difficulties existing in concatenating the proper functions, and in tuning the various parameters to overcome the GEE computational limits. In this context, this work is aimed at developing and testing an OO classification approach combining the Simple Non-Iterative Clustering (SNIC) algorithm to identify spatial clusters, the Gray-Level Co-occurrence Matrix (GLCM) to calculate cluster textural indices, and two ML algorithms (Random Forest (RF) or Support Vector Machine (SVM)) to perform the final classification. A Principal Components Analysis (PCA) is applied to the main seven GLCM indices to synthesize in one band the textural information used for the OO classification. The proposed approach is implemented in a user-friendly, freely available GEE code useful to perform the OO classification, tuning various parameters (e.g., choose the input bands, select the classification algorithm, test various segmentation scales) and compare it with a PB approach. The accuracy of OO and PB classifications can be assessed both visually and through two confusion matrices that can be used to calculate the relevant statistics (producer’s, user’s, overall accuracy (OA)). The proposed methodology was broadly tested in a 154 km2 study area, located in the Lake Trasimeno area (central Italy), using Landsat 8 (L8), Sentinel 2 (S2), and PlanetScope (PS) data. The area was selected considering its complex LULC mosaic mainly composed of artificial surfaces, annual and permanent crops, small lakes, and wooded areas. In the study area, the various tests produced interesting results on the different datasets (OA: PB RF (L8 = 72.7%, S2 = 82%, PS = 74.2), PB SVM (L8 = 79.1%, S2 = 80.2%, PS = 74.8%), OO RF (L8 = 64%, S2 = 89.3%, PS = 77.9), OO SVM (L8 = 70.4, S2 = 86.9%, PS = 73.9)). The broad code application demonstrated very good reliability of the whole process, even though the OO classification process resulted, sometimes, too demanding on higher resolution data, considering the available computational GEE resources.

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Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9100580 ◽

2020 ◽

Vol 9 (10) ◽

pp. 580 ◽

Cited By ~ 1

Author(s):

Maria Antonia Brovelli ◽

Yaru Sun ◽

Vasil Yordanov

Keyword(s):

Machine Learning ◽

Forest Dynamics ◽

Google Earth ◽

Classification Model ◽

Mitigation Measures ◽

Computational Time ◽

Forest Change ◽

Machine Learning Classification ◽

Cloud Processing ◽

Google Earth Engine

Deforestation causes diverse and profound consequences for the environment and species. Direct or indirect effects can be related to climate change, biodiversity loss, soil erosion, floods, landslides, etc. As such a significant process, timely and continuous monitoring of forest dynamics is important, to constantly follow existing policies and develop new mitigation measures. The present work had the aim of mapping and monitoring the forest change from 2000 to 2019 and of simulating the future forest development of a rainforest region located in the Pará state, Brazil. The land cover dynamics were mapped at five-year intervals based on a supervised classification model deployed on the cloud processing platform Google Earth Engine. Besides the benefits of reduced computational time, the service is coupled with a vast data catalogue providing useful access to global products, such as multispectral images of the missions Landsat five, seven, eight and Sentinel-2. The validation procedures were done through photointerpretation of high-resolution panchromatic images obtained from CBERS (China–Brazil Earth Resources Satellite). The more than satisfactory results allowed an estimation of peak deforestation rates for the period 2000–2006; for the period 2006–2015, a significant decrease and stabilization, followed by a slight increase till 2019. Based on the derived trends a forest dynamics was simulated for the period 2019–2028, estimating a decrease in the deforestation rate. These results demonstrate that such a fusion of satellite observations, machine learning, and cloud processing, benefits the analysis of the forest dynamics and can provide useful information for the development of forest policies.

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Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence

First Monday ◽

10.5210/fm.v24i2.8237 ◽

2019 ◽

Author(s):

Niel Chah

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Historical Context ◽

Historical Background ◽

Deep Roots ◽

Technical Guide ◽

Learning Machine ◽

High Level

Interest in deep learning, machine learning, and artificial intelligence from industry and the general public has reached a fever pitch recently. However, these terms are frequently misused, confused, and conflated. This paper serves as a non-technical guide for those interested in a high-level understanding of these increasingly influential notions by exploring briefly the historical context of deep learning, its public presence, and growing concerns over the limitations of these techniques. As a first step, artificial intelligence and machine learning are defined. Next, an overview of the historical background of deep learning reveals its wide scope and deep roots. A case study of a major deep learning implementation is presented in order to analyze public perceptions shaped by companies focused on technology. Finally, a review of deep learning limitations illustrates systemic vulnerabilities and a growing sense of concern over these systems.

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