Building Feature Extraction with Machine Learning: Geospatial Applications, 1st Edition

Name: Building Feature Extraction with Machine Learning: Geospatial Applications, 1st Edition
SKU: GC-585493304
Availability: InStock

$30.00

. Big geospatial datasets created by large infrastructure projects require massive computing resources to process. Feature extraction is a process used to reduce the initial set of raw data for manageable image processing, and machine learning (ML) is the science that supports it. This book focuses on feature extraction methods for optical geospatial data using ML. It is a practical guide for professionals and graduate students who are starting a career in information extraction. It explains spatial feature extraction in an easy-to-understand way and includes real case studies on how to collect height values for spatial features, how to develop 3D models in a map context, and others. Features Provides the basics of feature extraction methods and applications along with the fundamentals of machine learning Discusses in detail the application of machine learning techniques in geospatial building feature extraction Explains the methods for estimating object height from optical satellite remote sensing images using Python Includes case studies that demonstrate the use of machine learning models for building footprint extraction and photogrammetric methods for height assessment Highlights the potential of machine learning and geospatial technology for future project developments This book will be of interest to professionals, researchers, and graduate students in geoscience and earth observation, machine learning and data science, civil engineers, and urban planners. Building Feature Extraction with Machine Learning: Geospatial Applications 1st Edition is written by Bharath.H. Aithal; Prakash P.S. and published by CRC Press. ISBNs for Building Feature Extraction with Machine Learning are 9781000817201, 1000817202 and the print ISBNs are 9781032255330, 1032255331. Additional ISBNs include 9781003288046, 9781032263830, 9781000817195.

Order Processing Time

All orders placed on our website are processed within 2-4 business days, from Monday to Friday, 8:00 AM – 6:00 PM Pacific Time (PT). Orders received after our daily cut-off time of 10:00 PM PT will be processed on the next business day. Please note that we do not process orders on weekends or public holidays.

Shipping Methods and Carriers

Zetlly partners exclusively with reputable shipping carriers to ensure timely delivery of your orders. We utilize:

FedEx
UPS
USPS

The choice of carrier is determined by factors such as destination, weight, and delivery timeframe to provide optimal service.

Shipping Rates and Fees

Free shipping is provided for all orders over $199.
Orders under $199 will incur a flat-rate shipping fee of $7.99.
All orders shipped within the United States will be subject to a sales tax charge of 5%.

Estimated Delivery Time

Once shipped, orders typically arrive within 6 to 10 business days. Our delivery times are from Monday to Friday, 8:00 AM – 6:00 PM Pacific Time (PT). Please allow additional time for deliveries to remote or rural locations.

Shipping Restrictions

Zetlly currently ships exclusively within the United States. At present, we do not offer international shipping or deliveries to P.O. boxes or APO/FPO addresses. Orders placed with addresses outside our designated delivery areas will be canceled, and refunds will be processed accordingly.

Tracking Your Order

Upon shipment, customers will receive a confirmation email containing tracking information. You can track your order directly through the provided tracking link or by visiting the carrier’s official website:

Please allow up to 48 hours for tracking information to update in the carrier’s system.

Eligibility for Returns and Exchanges

We accept returns and exchanges within 30 days from the date your order is delivered. Items must be unused, in the original condition, and accompanied by the original packaging and receipt or proof of purchase.

How to Return or Exchange an Item

To initiate a return or exchange, please follow these steps:

Contact our customer support at contact@zetlly.com with your order number and reason for return or exchange.
Our team will respond within 24 hours to provide detailed instructions, including the specific Return Address for your shipment.
Package your item securely and include all original packaging and proof of purchase.

Return shipments should be sent to: Blanq LLC 1201 South Hope Street Apt 2413, Los Angeles, CA 90015, USA

Return Conditions

Items must be returned in their original condition, unworn, undamaged, and complete with all original packaging and documentation.
Items returned without prior authorization or not meeting the above conditions may not qualify for a refund or exchange.

Return Shipping Costs

Customers are responsible for return shipping costs unless the return is due to our error or a defective product. We recommend using a trackable shipping service to ensure your return reaches us safely.

Non-Returnable Items

The following items cannot be returned:

Digital products (e-books or downloadable content)
Personalized or customized items
Gift cards

Accepted Payment Methods

Zetlly accepts the following secure and widely trusted payment options:

PayPal: Easily pay through your PayPal account, benefiting from secure transactions and buyer protection.
Stripe: Pay securely using major credit and debit cards including Visa, MasterCard, American Express, and Discover via Stripe’s encrypted payment gateway.

Payment Security

At Zetlly, your security is our utmost priority. We utilize advanced encryption technologies and robust security protocols provided by PayPal and Stripe. All payment information entered on our site is encrypted using Secure Socket Layer (SSL) technology, ensuring your financial information remains private and secure throughout the transaction process.

Zetlly does not store any credit card or sensitive financial information directly on our servers, further enhancing the security and protection of your personal data.

Payment Process and Confirmation

Upon placing an order, your chosen payment method (PayPal or Stripe) will immediately process the transaction. You will receive an automated confirmation email shortly after your payment has been successfully completed, detailing your transaction and order summary.

Please retain this confirmation email for your records and reference in case of any inquiries or disputes.

.
Big geospatial datasets created by large infrastructure projects require massive computing resources to process. Feature extraction is a process used to reduce the initial set of raw data for manageable image processing, and machine learning (ML) is the science that supports it. This book focuses on feature extraction methods for optical geospatial data using ML. It is a practical guide for professionals and graduate students who are starting a career in information extraction. It explains spatial feature extraction in an easy-to-understand way and includes real case studies on how to collect height values for spatial features, how to develop 3D models in a map context, and others. Features Provides the basics of feature extraction methods and applications along with the fundamentals of machine learning Discusses in detail the application of machine learning techniques in geospatial building feature extraction Explains the methods for estimating object height from optical satellite remote sensing images using Python Includes case studies that demonstrate the use of machine learning models for building footprint extraction and photogrammetric methods for height assessment Highlights the potential of machine learning and geospatial technology for future project developments This book will be of interest to professionals, researchers, and graduate students in geoscience and earth observation, machine learning and data science, civil engineers, and urban planners.
Building Feature Extraction with Machine Learning: Geospatial Applications 1st Edition is written by Bharath.H. Aithal; Prakash P.S. and published by CRC Press. ISBNs for Building Feature Extraction with Machine Learning are 9781000817201, 1000817202 and the print ISBNs are 9781032255330, 1032255331. Additional ISBNs include 9781003288046, 9781032263830, 9781000817195.