Sistemas de Información Geográfica (GIS en inglés)

Publicly Available Geographic Information Sources and Common Analysis Tools

Satbyeol Shin and Young Gu Her

Mapping the US Census Data Using the TIGER/Line Shapefiles

Young Gu Her and Ziwen Yu

This publication introduces the TIGER/Line Shapefile datasets and demonstrates how we can extract and map spatial information from the US Census data using the shapefiles. Written by Young Gu Her and Ziwen Yu, and published by the UF/IFAS Department of Agricultural and Biological Engineering, May 2021.

A Step-by-Step Guide for Automated Plant Canopy Delineation Using Deep Learning: An Example in Strawberry Using ArcGIS Pro Software

Amr Abd-Elrahman, Katie Britt, and Vance Whitaker

This publication presents a guide to image analysis for researchers and farm managers who use ArcGIS software. Anyone with basic geographic information system analysis skills may follow along with the demonstration and learn to implement the Mask Region Convolutional Neural Networks model, a widely used model for object detection, to delineate strawberry canopies using ArcGIS Pro Image Analyst Extension in a simple workflow. This process is useful for precision agriculture management.

An Introduction to Freely Available Street Network Data

Hartwig Hochmair and Dennis Zielstra

Agricultural, natural resource management, urban planning, and community development projects often involve analysis and mapping of spatial data. This publication focuses on two repositories of freely available street network data. It describes their data retrieval process, and points out some of their differences. Two sample applications demonstrate the usefulness of free street data in various projects.

Deep Learning Classification of High-Resolution Drone Images Using the ArcGIS Pro Software

Amr Abd-Elrahman, Katie Britt, and Tao Liu

Deep learning classification of invasive species using widely used ArcGIS Pro software and increasingly common drone imagery can aid in identification and management of natural areas. A step-by-step implementation, with associated data for users to access, is presented to make this technology more widely accessible to GIS analysts, researchers, and graduate students working with remotely sensed data in the natural resource field.