eBook
Explainable Machine Learning for Geospatial Data Analysis : A Data-Centric Approach
| Τίτλος: | Explainable Machine Learning for Geospatial Data Analysis : A Data-Centric Approach |
|---|---|
| Περιγραφή: | Explainable machine learning (XML), a subfield of AI, is focused on making complex AI models understandable to humans. This book highlights and explains the details of machine learning models used in geospatial data analysis. It demonstrates the need for a data-centric, explainable machine learning approach to obtain new insights from geospatial data. It presents the opportunities, challenges, and gaps in the machine and deep learning approaches for geospatial data analysis and how they are applied to solve various environmental problems in land cover changes and in modeling forest canopy height and aboveground biomass density. The author also includes guidelines and code scripts (R, Python) valuable for practical readers.Features Data-centric explainable machine learning (ML) approaches for geospatial data analysis. The foundations and approaches to explainable ML and deep learning. Several case studies from urban land cover and forestry where existing explainable machine learning methods are applied. Descriptions of the opportunities, challenges, and gaps in data-centric explainable ML approaches for geospatial data analysis. Scripts in R and python to perform geospatial data analysis, available upon request. This book is an essential resource for graduate students, researchers, and academics working in and studying data science and machine learning, as well as geospatial data science professionals using GIS and remote sensing in environmental fields. |
| Συγγραφείς: | Courage Kamusoko |
| Resource Type: | eBook. |
| Θέματα: | Geospatial data--Computer processing, Machine learning |
| Categories: | TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems, TECHNOLOGY & ENGINEERING / Environmental / General, COMPUTERS / Data Science / Machine Learning |
| Βάση Δεδομένων: | eBook Index |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: edsebk DbLabel: eBook Index An: 3965579 RelevancyScore: 975 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 974.776672363281 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Explainable Machine Learning for Geospatial Data Analysis : A Data-Centric Approach – Name: Abstract Label: Description Group: Ab Data: Explainable machine learning (XML), a subfield of AI, is focused on making complex AI models understandable to humans. This book highlights and explains the details of machine learning models used in geospatial data analysis. It demonstrates the need for a data-centric, explainable machine learning approach to obtain new insights from geospatial data. It presents the opportunities, challenges, and gaps in the machine and deep learning approaches for geospatial data analysis and how they are applied to solve various environmental problems in land cover changes and in modeling forest canopy height and aboveground biomass density. The author also includes guidelines and code scripts (R, Python) valuable for practical readers.Features Data-centric explainable machine learning (ML) approaches for geospatial data analysis. The foundations and approaches to explainable ML and deep learning. Several case studies from urban land cover and forestry where existing explainable machine learning methods are applied. Descriptions of the opportunities, challenges, and gaps in data-centric explainable ML approaches for geospatial data analysis. Scripts in R and python to perform geospatial data analysis, available upon request. This book is an essential resource for graduate students, researchers, and academics working in and studying data science and machine learning, as well as geospatial data science professionals using GIS and remote sensing in environmental fields. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Courage+Kamusoko%22">Courage Kamusoko</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Geospatial+data--Computer+processing%22">Geospatial data--Computer processing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22TECHNOLOGY+%26+ENGINEERING+%2F+Remote+Sensing+%26+Geographic+Information+Systems%22">TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems</searchLink><br /><searchLink fieldCode="ZK" term="%22TECHNOLOGY+%26+ENGINEERING+%2F+Environmental+%2F+General%22">TECHNOLOGY & ENGINEERING / Environmental / General</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+Machine+Learning%22">COMPUTERS / Data Science / Machine Learning</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=3965579 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 910.285631 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Geospatial data--Computer processing Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Explainable Machine Learning for Geospatial Data Analysis : A Data-Centric Approach Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Courage Kamusoko – PersonEntity: Name: NameFull: Courage Kamusoko IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2024 – D: 18 M: 11 Type: profile Y: 2024 Identifiers: – Type: isbn-print Value: 9781032503806 – Type: isbn-electronic Value: 9781003398257 – Type: isbn-electronic Value: 9781040252468 – Type: isbn-electronic Value: 9781040252512 Titles: – TitleFull: Explainable Machine Learning for Geospatial Data Analysis : A Data-Centric Approach Type: main |
| ResultId | 1 |