PyGEE-ST-MEDALUS: AI Spatiotemporal Framework Integrating MODIS and Sentinel-1/-2 Data for Desertification Risk Assessment in Northeastern Algeria

Improved machine-learning mapping of local climate zones in metropolitan areas using composite Earth observation data in Google Earth Engine

This work aims to expose the contribution of the use of the cloud google earth Engine (GEE) platform, in particular the capacity of optical monitoring by remote sensing to assess the impact of environmental changes on the evolution of natural resources in the Middle Atlas region. To achieve this goal, the dense time stacking of multi-temporal Landsat images and random forest algorithm based on the Google Earth Engine (GEE) platform was used. The spatial resolution of the images used is 30 meters for the TM 5 sensor (Thematic Mapper) and the OLI 8 sensor (Operational Land Imager). Further, the google earth engine platform is used primarily to download and prepare the images for the dates 1986, 2000, and 2019, then a supervised classification with the Random Forest (RF) algorithm to produce land use maps of selected dates with an overall accuracy exceeding 80%. This was followed by the production of maps and change matrices for the periods 1986-2000 and 2000-2019. The results obtained have shown a decline in grassland, forest land, and water body in parallel with an increase in the following classes: buildings, farmland, and arboriculture during the last 30 years. In addition, elevation was the most important characteristic variable for land-use classification in the study area. Obtained results provide theoretical support for adjusting and optimizing land use in the High Oum Er-Rbia watershed.

Intertwined impacts of urbanization and land cover change on urban climate and agriculture in Aurangabad city (MS), India using google earth engine platform

Leveraging Google Earth Engine platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa

Population growth, urbanization, policy changes, economic activities, agriculture, infrastructure development, and climate change are some of the factors that can lead to land cover changes. This necessitates serious monitoring to determine the extent of land changes occurring. Semarang City is one of the cities that has undergone significant land changes. This can be seen from the substantial areas that have undergone land-use conversion. This study aims to observe land cover changes in Semarang City using Landsat 8 TOA satellite imagery analyzed through the Google Earth Engine (GEE) platform. GEE is an alternative for image processing as it simplifies the process of image analysis compared to conventional desktop-based image processing methods. The classification is performed using a machine learning algorithm with the Classification and Regression Trees (CART) method tha available in GEE. The accuracy of the classification is tested using the confusion matrix calculation. The results obtained show the accuracy of land cover change testing for the years 2013, 2016, 2019, and 2022, with kappa accuracies reaching 96.7%, 93.78%, 94.54%, and 96.04%, respectively. The effectiveness of image processing on the GEE platform shows that GEE can be used as a fast and efficient alternative for image processing. Based on the research findings, it is shown that residential areas experience significant increases every year. Therefore, the increase in residential areas has a significant impact on the surrounding environment, such as increasing urban temperatures, which reduces the comfort level of residents, especially in Semarang City. Keywords : Land Cover, Landsat 8 Satellite Imagery, Google Earth Engine ABSTRAK Pertumbuhan populasi, urbanisasi, perubahan kebijakan, aktivitas ekonomi, pertanian, pengembangan infrastruktur, dan perubahan iklim adalah beberapa faktor yang dapat menyebabkan perubahan tutupan lahan. Hal ini memerlukan pemantauan yang serius untuk melihat seberapa besar perubahan lahan yang terjadi. Kota Semarang merupakan salah satu kota yang telah banyak mengalami perubahan lahan. Hal ini dapat dilihat dari banyaknya wilayah yang telah beralih fungsi lahan. Penelitian ini bertujuan untuk melihat perubahan tutupan lahan di Kota Semarang dengan menggunakan data citra satelit Landsat 8 TOA yang di analisis menggunakan platform Google Earth Engine (GEE). GEE menjadi alternatif pengolahan citra karena memudahkan pengguna dalam melakukan pengolahan dan analisis citra dibandingkan dengan metode konvensional pengolahan citra berbasis desktop. Klasifikasi dilakukan dengan algoritma machine learning menggunakan metode Classification and Regression Trees (CART) yang tersedia di GEE. Uji akurasi klasifikasi dilakukan dengan menggunakan perhitungan confusion matrix. Hasilnya diperoleh uji akurasi perubahan tutupan lahan pada tahun 2013, 2016, 2019 dan 2022, akurasi kappa masing-masing mencapai 96,7%, 93,78%, 94,54%, dan 96,04%. Efektifitas pengolahan citra di platform GEE menunjukkan bahwa GEE dapat digunakan sebagai alternatif dalam pengolahan citra yang cepat dan efisien. Berdasarkan hasil penelitian menunjukkan bahwa wilayah pemukiman mengalami kenaikan yang signifikan setiap tahun Oleh karena itu, peningkatan pemukiman memberi dampak yang signifikan terhadap lingkungan sekitar, seperti peningkatan suhu kota, yang menyebabkan tingkat kenyamanan penduduk semakin berkurang, terutama di Kota Semarang. Keywords : Tutupan Lahan, Citra Satelit Landsat 8, Google Earth Engine.

The world around us is constantly changing, and humans contribute to many of these changes. Land cover and land use (LCLU) changes over time have a significant impact on the functioning of the Earth, particularly climate change and global warming. Spatial data of LCLU changes find important applications in land management, monitoring the sustainable development of agriculture, forestry, rural areas, assessing the state of biodiversity and urban planning.In the frame of the InCoNaDa project "Enhancing the user uptake of Land Cover / Land Use information derived from the integration of Copernicus services and national databases”, the maps of land cover (LC) changes were developed for two study areas - the Łódź Voivodeship in Poland and the Viken County in Norway. The detection of LC changes was performed on the annual bases for the period 2018-2021 based on the analysis of multitemporal optical data from the Sentinel-2 mission. The Google Earth Engine (GEE) platform was used, which allows to analyze satellite data and to perform spatial analyses anywhere in the World while providing computing power. The LC change detection method was divided into two phases. The first phase is based on the analysis of spectral signatures, and the second phase applies the machine learning Random Forest algorithm. The classification was performed separately for each time interval: 2018-2019, 2019-2020, 2020-2021. In this way, three independent classification models were developed for each study area. The following three LC change classes were distinguished:  a) no-change, b) forest loss, and c) construction sites and newly built-up areas. The minimum mapping unit (MMU) was 0.2 ha. The LC change detection models reached high accuracy - in both study areas for all time intervals, the overall accuracy was equal to or greater than 0.97 and the Kappa coefficient than 0.95. The independent verification carried out based on the aerial orthophotos proved that the overall accuracy of the LC changes is pretty good for both study areas (around 0.9). The changes occurring in the construction sites and newly built-up area class reached slightly lower accuracy and has the lowest precision. The presented method showed its universality and adaptability, giving the possibility for further development. We will present the method, algorithm, results and their verification for Poland and Norway.

Simple SummaryForecasting dengue cases often face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without historical dengue cases. With the advance of the geospatial big data cloud computing in Google Earth Engine and deep learning, this study proposed an efficient framework of dengue prediction at an epidemiological week basis using geospatial big data analysis in Google Earth Engine and Long Short Term Memory modeling. We focused on the dengue epidemics in the Federal District of Brazil during 2007–2019. Based on Google Earth Engine and epidemiological calendar, we computed the weekly composite for each dengue driving factor, and spatially aggregated the pixel values into dengue transmission areas to generate the time series of driving factors. A multi-step-ahead Long Short Term Memory modeling was used, and the time-differenced natural log-transformed dengue cases and the time series of driving factors were considered as outcomes and explantary factors, respectively, with two modeling scenarios (with and without historical cases). The performance is better when historical cases were used, and the 5-weeks-ahead forecast has the best performance.Timely and accurate forecasts of dengue cases are of great importance for guiding disease prevention strategies, but still face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation capability due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without the application of historical case information. Geospatial big data, cloud computing platforms (e.g., Google Earth Engine, GEE), and emerging deep learning algorithms (e.g., long short term memory, LSTM) provide new opportunities for advancing these efforts. Here, we focused on the dengue epidemics in the urban agglomeration of the Federal District of Brazil (FDB) during 2007–2019. A new framework was proposed using geospatial big data analysis in the Google Earth Engine (GEE) platform and long short term memory (LSTM) modeling for dengue case forecasts over an epidemiological week basis. We first defined a buffer zone around an impervious area as the main area of dengue transmission by considering the impervious area as a human-dominated area and used the maximum distance of the flight range of Aedes aegypti and Aedes albopictus as a buffer distance. Those zones were used as units for further attribution analyses of dengue epidemics by aggregating the pixel values into the zones. The near weekly composite of potential driving factors was generated in GEE using the epidemiological weeks during 2007–2019, from the relevant geospatial data with daily or sub-daily temporal resolution. A multi-step-ahead LSTM model was used, and the time-differenced natural log-transformed dengue cases were used as outcomes. Two modeling scenarios (with and without historical dengue cases) were set to examine the potential of historical information on dengue forecasts. The results indicate that the performance was better when historical dengue cases were used and the 5-weeks-ahead forecast had the best performance, and the peak of a large outbreak in 2019 was accurately forecasted. The proposed framework in this study suggests the potential of the GEE platform, the LSTM algorithm, as well as historical information for dengue risk forecasting, which can easily be extensively applied to other regions or globally for timely and practical dengue forecasts.

Forest fires are major disturbances in forest ecosystems. The rapid detection of the spatial and temporal characteristics of fires is essential for formulating targeted post-fire vegetation restoration measures and assessing fire-induced carbon emissions. We propose an accurate and efficient framework for extracting the spatiotemporal characteristics of fires using vegetation change tracker (VCT) products and the Google Earth Engine (GEE) platform. The VCT was used to extract areas of persistent forest and forest disturbance patches from Landsat images of Xichang and Muli, Liangshan prefecture, Sichuan province in southwestern China and Huma, Heilongjiang province, in northeastern China. All available Landsat images in the GEE platform in a year were normalized using the VCT-derived persisting forest mask to derive three standardized vegetation indices (normalized burn ratio (NBRr), normalized difference moisture index (NDMIr), and normalized difference vegetation index (NDVIr)). Historical forest disturbance events in Xichang were used to train two decision trees using the C4.5 data mining tool. The differenced NBRr, NDMIr, and NDVIr (dNBRr, dNDMIr, and dNDVIr) were obtained by calculating the difference in the index values between two temporally adjacent images. The occurrence time of disturbance events were extracted using the thresholds identified by decision tree 1. The use of all available images in GEE narrowed the disturbance occurrence time down to 16 days. This period was extended if images were not available or had cloud cover. Fire disturbances were distinguished from other disturbances by comparing the dNBRr, dNDMIr, and dNDVIr values with the thresholds identified by decision tree 2. The results showed that the proposed framework performed well in three study areas. The temporal accuracy for detecting disturbances in the three areas was 94.33%, 90.33%, and 89.67%, the classification accuracy of fire and non-fire disturbances was 85.33%, 89.67%, and 83.67%, and the Kappa coefficients were 0.71, 0.74, and 0.67, respectively. The proposed framework enables the efficient and rapid extraction of the spatiotemporal characteristics of forest fire disturbances using frequent Landsat time-series data, GEE, and VCT products. The results can be used in forest fire disturbance databases and to implement targeted post-disturbance vegetation restoration practices.

Nitrogen plays an important role in improving soil productivity and maintaining ecosystem stability. Mapping and monitoring the soil total nitrogen (STN) content is the basis for modern soil management. The Google Earth Engine (GEE) platform covers a wide range of available satellite remote sensing datasets and can process massive data calculations. We collected 6823 soil samples in Shandong Province, China. The random forest (RF) algorithm predicted the STN content in croplands from 2002 to 2016 in Shandong Province, China on the GEE platform. Our results showed that RF had the coefficient of determination (R2) (0.57), which can predict the spatial distribution of the STN and analyze the trend of STN changes. The remote sensing spectral reflectance is more important in model building according to the variable importance analysis. From 2002 to 2016, the STN content of cropland in the province had an upward trend of 35.6%, which increased before 2010 and then decreased slightly. The GEE platform provides an opportunity to map dynamic changes of the STN content effectively, which can be used to evaluate soil properties in the future long-term agricultural management.

Assessing the performance of machine learning algorithms for soil salinity mapping in Google Earth Engine platform using Sentinel-2A and Landsat-8 OLI data

The field of credit score classification has experienced notable progress through the introduction of deep learning (DL) and machine learning (ML) techniques, empowering financial institutions to make well-informed decisions about assessing creditworthiness. However, existing research often focuses on just a few classifiers pertaining to either ML or DL techniques, lacking a comprehensive comparative analysis between the two. This gap calls for a thorough study that evaluates and compares a wide range of ML classifiers and DL models in the context of credit scoring. Our work aims to address this limitation by presenting an extensive comparative analysis between different ML and DL approaches. We provide novel insights into the strengths and weaknesses of each model, enabling financial institutions to select the most suitable approach for their specific needs. Through conducting extensive experiments on a credit records dataset, we evaluated the accuracy, precision, recall, and F1 score of various ML classifiers, such as logistic regression, decision trees, and random forests. Additionally, we delved into the capabilities of DL models, which included multi-layer perceptron (MLP), convolutional neural networks (CNN), recurrent neural networks (RNN), and hybrid models. Our findings revealed that Random Forest achieved the highest test accuracy of 90.27, while MLP and CNN closely followed with the second-highest accuracies at 87.08 and 87.16, respectively. These results also demonstrated the potential of both MLP and CNN in credit scoring assessment. MLP's strength lies in its capacity to handle non-linear relationships between features, providing a viable alternative to decision tree-based models. On the other hand, CNN excels in capturing spatial patterns and dependencies among features, presenting a distinct advantage in credit score classification. Overall, our study presents a broad-spectrum overview of the analysis, encompassing each model's performance and effectiveness in credit score classification. The findings empower financial institutions to leverage the benefits of DL and ML techniques, optimizing their decision-making processes and enhancing risk management strategies. By selecting the most suitable credit score classification model based on the insights gained from this comparative analysis, institutions can make informed choices and effectively evaluate creditworthiness, leading to improved risk assessment and lending decisions.

The most dangerous disorders include melanoma. Yet, a precise diagnosis of skin cancer is difficult. Recent research has shown that a variety of activities can be performed better using deep learning and machine learning techniques. For skin conditions, these algorithms are highly useful. In this article, we examine various deep learning and machine learning techniques and how they could be applied to the detection of melanoma. This paper provides a number of publicly downloadable datasets, information on common melanoma, instructions for getting dermatology pictures, and more. Once machine learning and deep learning concepts have been introduced, our attention shifts to analysing common machine learning and deep learning architectures as well as popular frameworks for putting machine and deep learning algorithms into practice. Metrics for performance evaluation are then offered. In this section, we will cover the research on machine learning and deep learning and how they can be applied to the detection of melanoma skin illnesses. We also go over potential research avenues and the difficulties in the field. The main objective of this work is to discuss modern machine learning and deep learning techniques for melanoma diagnosis.

With the growth of cloud computing, the use of the Google Earth Engine (GEE) platform to conduct research on water inversion, natural disaster monitoring, and land use change using long time series of Landsat images has also gradually become mainstream. Landsat images are currently one of the most important image data sources for remote sensing inversion. As a result of changes in time and weather conditions in single-view images, varying image radiances are acquired; hence, using a monthly or annual time scale to mosaic multi-view images results in strip color variation. In this study, the NDWI and MNDWI within 50 km of the coastline of the Yucatán Peninsula from 1993 to 2021 are used as the object of study on GEE platform, and mosaic areas with chromatic aberrations are reconstructed using Landsat TOA (top of atmosphere reflectance) and SR (surface reflectance) images as the study data. The DN (digital number) values and probability distributions of the reference image and the image to be restored are classified and counted independently using the random forest algorithm, and the classification results of the reference image are mapped to the area of the image to be restored in a histogram-matching manner. MODIS and Sentinel-2 NDWI products are used for comparison and validation. The results demonstrate that the restored Landsat NDWI and MNDWI images do not exhibit obvious band chromatic aberration, and the image stacking is smoother; the Landsat TOA images provide improved results for the study of water bodies, and the correlation between the restored Landsat SR and TOA images with the Sentinel-2 data is as high as 0.5358 and 0.5269, respectively. In addition, none of the existing Landsat NDWI products in the GEE platform can effectively eliminate the chromatic aberration of image bands.

Assessing the impact of unplanned settlements on urban renewal projects with GEE

Land use and land cover (LULC) conversion has become a chronic problem in Nan province. The primary factors of changes are lacking arable land, agricultural practices, and agriculture expansion. This study evaluated the usefulness of multi-sensor Landsat-5 (LS5), Landsat-8 (LS8), Sentinel-1 (S1), and Sentinel-2 (S2) satellite data for monitoring changes in LULC in Nan province, Thailand during a 30-year period (1990-2019), using a random forest (RF) model and the cloud-based Google Earth Engine (GEE) platform. Information of established land management policies was also used to describe the LULC changes. The median composite of the input variables selection from multi-sensor data were used to generate datasets. A total of 36 datasets showed the overall accuracy (OA) ranged from 51.70% to 96.95%. Sentinel-2 satellite images combined with the Modified Soil-Adjusted Vegetation Index (MSAVI) and topographic variables provided the highest OA (96.95%). Combination of optical (i.e., S2 and LS8) and S1 Synthetic Aperture Radar (SAR) data expressed better classification accuracy than individual S1 data. Forest cover decreased continuously during five consecutive periods. Coverage of maize and Pará rubber trees rapidly expanded in 2010-2014. These changes indicate an adverse consequence of the established economic development promoted by industrial and export agriculture. The findings strongly support the use of the RF technique, GEE platform and multi-sensor satellite data to enhance LULC classification accuracy in mountainous area. This study recommended that certain informative and science-based evidence will encourage local policymakers to identify priority areas for land management and natural resource conservation.