Generation of a Land Cover Atlas of environmental critic zones using unconventional tools

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Prediction of Housing Price and Forest Cover Using Mosaics with Uncertain Satellite Imagery

The growing world is more expensive to estimate land use, road length, and forest cover using a plant-scaled ground monitoring system. Satellite imaging contains a significant amount of detailed uncertain information. Combining this with machine learning aids in the organization of these data and the estimation of each variable separately. The resources necessary to deploy Machine learning technologies for Remote sensing images, on the other hand, restrict their reach ability and application. Based on satellite observations which are notably underutilised in impoverished nations, while practical competence to implement SIML might be restricted. Encoded forms of images are shared across tasks, and they will be calculated and sent to an infinite number of researchers who can achieve top-tier SIML performance by training a regression analysis onto the actual data. By separating the duties, the proposed SIML solution, MOSAIKS, shapes SIML approachable and global. A Featurization stage turns remote sensing data into concise vector representations, and a regression step makes it possible to learn the correlations which are specific to its particular task which link the obtained characteristics to the set of uncertain data

Optimized Deep Belief Neural Network for Semantic Change Detection in Multi-Temporal Image

Nowadays, a massive quantity of remote sensing images is utilized from tremendous earth observation platforms. For processing a wide range of remote sensing data to be transferred based on knowledge and information of them. Therefore, the necessity for providing the automated technologies to deal with multi-spectral image is done in terms of change detection. Multi-spectral images are associated with plenty of corrupted data like noise and illumination. In order to deal with such issues several techniques are utilized but they are not effective for sensitive noise and feature correlation may be missed. Several machine learning-based techniques are introduced to change detection but it is not effective for obtaining the relevant features. In other hand, the only limited datasets are available in open-source platform; therefore, the development of new proposed model is becoming difficult. In this work, an optimized deep belief neural network model is introduced based on semantic modification finding for multi-spectral images. Initially, input images with noise destruction and contrast normalization approaches are applied. Then to notice the semantic changes present in the image, the Semantic Change Detection Deep Belief Neural Network (SCD-DBN) is introduced. This research focusing on providing a change map based on balancing noise suppression and managing the edge of regions in an appropriate way. The new change detection method can automatically create features for different images and improve search results for changed regions. The projected technique shows a lower missed finding rate in the Semantic Change Detection dataset and a more ideal rate than other approaches

Techniques and Challenges of the Machine Learning Method for Land Use/Land Cover (LU/LC) Classification in Remote Sensing Using the Google Earth Engine

In order to accurately observe the globe, land use and land cover are crucial. Due to the proliferation of several global modifications associated with the existence of the planet, land use/land cover (LU/LC) classification is now regarded as a topic of highest significance in the natural environment and an important field to be researched by researchers. Google Earth provides satellite image dataset which contains high-resolution images; these images are used to analyze the land area. In order to address the dearth of review articles throughout the land use/land cover classification phase, we proposed a full evaluation, which might help researchers continue their work. Therefore, the purpose of this study is to investigate the methodical steps involved in classifying land use and land cover utilizing the Google Earth platform. The most widely used techniques researchers employ to achieve LU/LC classification using Google Earth Engine are examined in this work. The classification of land use and land cover for a specific region using time series was covered in this study, along with the many types of land use and land cover classes and the approach employed by Google Earth. The limits of the GEE tool and difficulties encountered during the process of classifying land use and cover have also been covered in this survey document. The importance of this review rests in inspiring future scholars to tackle the LU/LC analysis problem successfully, and this study offers researchers a road map for assessing land use/land cover classification

Sustainable Urbanization in the China‐Indochinese Peninsula Economic Corridor

Countries in the China‐Indochinese Peninsula are home to rich human and natural resource endowments and have the potential to be one of the world\u27s fastest growing areas. Sustainable urbanization in the China‐Indochinese Peninsula Economic Corridor is important for the regional economic development and prosperity. Taking the advantages of the remote sensing and Geographic Information System (GIS) technologies, this chapter is first presents a general overview of urbanization procession in this region and monitors the spatiotemporal dynamics of the urban environment; the second objective is to present the multiple driving force factor analysis for urban development in countries of the China‐Indochinese Peninsula Economic Corridor using statistical models. The results indicated that the China‐Indochinese Peninsula Economic Corridor has experienced a rapid urbanization process during the past 15 years both in terms of urban areas and urban population (UP). In addition to socioeconomic factors, there is also a noticeable correlation between foreign direct investment (FDI) and international trade and urban development in the China‐Indochinese Peninsula Economic Corridor. Active participation in international trade and attracting foreign investment are helpful for the regional urbanization. As a neighboring country, China\u27s economic and trade activity also has a significant impact on the urbanization in countries of the China‐Indochinese Peninsula Economic Corridor. Furthermore, as the launch of the Silk Road Economic Belt and the 21st Century Maritime Silk Road and the Asian Infrastructure Investment Bank (AIIB), the China‐Indochinese Peninsula Economic Corridor will witness a more rapid urbanization progress in the next decade. This study has its characteristics in focusing on the region of the Indochinese Peninsula in which the most rapid urbanization is occurring, presenting the state‐of‐the‐art techniques for monitoring urban expansion and probing into the driving factors of the urban expansion in the China‐Indochinese Peninsula Economic Corridor by multiple principles and multiple‐level data. It is expected to benefit policymakers in urban development and also provide a basis for further studies of sustainable urbanization in the China‐Indochinese Peninsula Economic Corridor

An empirical study of image processing methods for land cover classification and forest cover change detection in Northeastern Oregon\u27s timber resource-dependent communities (1986-2011)

A study was performed to evaluate remote sensing methods for classifying land cover and land cover change throughout a two-county area in Northeastern Oregon (1986-2011). In the past three decades, this region has seen significant changes in forest management -- changes that can be readily identified from the synoptic perspective. This study employs an accuracy assessment-based empirical approach to test a number of advanced digital image processing techniques that have recently emerged in the field of remote sensing. The accuracies are assessed using traditional and area-based error matrices. It was determined that, for single-time land cover classification, Bayes pixel-based classification using samples created with segmentation parameters of scale 8 and shape 0.3 resulted in the highest overall accuracy. For land cover change detection, it was determined that Landsat 5 TM band 7 with a change threshold of 1.75 SD resulted in the highest accuracy for forest harvesting detection

An integrated study of earth resources in the State of California based on ERTS-1 and supporting aircraft data, volume 1

There are no author-identified significant results in this report

Application of remote sensing to selected problems within the state of California

There are no author-identified significant results in this report

Monitoring Of Irrigated Areas In Gujarat State Using Gee Cloud Based Algorithm

The main aim of this study to identify the irrigated areas in Gujarat state using GEE using Sentinel-2 satellite imagery for crop year 2018-19. Traditionally, the classification is carried by downloading satellite images from available websites and processing of images in available software like Erdas, ArcGIS etc. The freely available high spatial resolution satellite datasets like Landsat-8, Sentinel -1 and Sentinel-2 consumes large amount of storage and also requires high end computers for processing and analyzing. In order to overcome some of the difficulties, Google Earth Engine (GEE), the most advanced cloud-based geospatial processing platform is being used. The download of satellite imagery, image processing and image classification etc. will be carried out in GEE with the help of Random Forest Algorithm. The results include LULC map, Rice crop extent map, Identification of rice crop extent The above maps will be validated using independent samples. These results help Government agencies and policy makers for quick decision making and implementation of their programme