£110.37

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£110 today · all-time low £108 (Sep 2026) · usually £109

Springer Digital Mapping of Soil Landscape Parameters - Machine Learning Guide

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Description

This Springer academic text explores the geospatial mapping of soil-landscape parameters using advanced machine learning and geomatics techniques. The book establishes fundamental concepts before demonstrating how machine learning algorithms and geomatics tools can enhance mapping accuracy and deepen understanding of soil management across landscapes. As rapid global urbanisation intensifies the need for sustainable land use and resource monitoring, this volume provides essential guidance on applying machine learning to analyse geospatial data for soil characterisation, landscape assessment, and environmental management. Part of the Studies in Big Data series (Volume 72), it bridges theoretical principles with practical geospatial applications, offering researchers, environmental scientists, and geomatics professionals a comprehensive resource for implementing data-driven approaches to soil-landscape parameter mapping.

Key Features

Covers fundamental concepts of soil-landscape parameter mapping in geospatial domains using machine learning methodologies.

Demonstrates practical applications of geomatics and machine learning for improved mapping accuracy and land management decision-making.

Addresses contemporary challenges in sustainable land use through continuous resource monitoring and geospatial analysis.

Part of Springer's Studies in Big Data series (Volume 72), providing peer-reviewed academic research on geospatial big data applications.

Includes machine learning techniques for analysing remote sensing and geospatial datasets to enhance soil characterisation and landscape understanding.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
21 February 2021
Listed Since
26 January 2021

Barcode

No barcode data available

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