LAWRENCE — Fall break is barely behind us, but a group of University of Kansas students has just finished an innovative eight-week course in using drones to develop aerial maps. Over the past two months, they’ve visited sites in KU's West District and at the Baker Wetlands, taking still images and videos over those areas. “The drone mapping course has been excellent in providing a hands-on experience with the drones,” said Siddharth Shankar, graduate student from Lucknow, India. “The course has focused not just on drones and how to fly them but also has made us aware of the FAA rules and regulations about drone flying and safety precautions. “My research has been in glaciology, with the study of icebergs in Greenland. The drone mapping course has provided new insights into incorporating it with my research in the near future.” The course, offered annually during the fall semester, is designed to teach students about the rapidly growing technology of small unmanned aerial systems, referred to as drones, and its wide-ranging applications — which include search-and-rescue, real estate and environmental monitoring. Students in the course come from a variety of disciplines including geography & atmospheric science, geology, ecology & evolutionary biology and civil engineering. Enthusiasm for the course has been very high, and it has filled rapidly each time it has been offered.

Mapping between the Course-IDs and the corresponding Organization-IDs in WS19

Mapping between the Course-IDs and the corresponding Organization-IDs in WS13

This dataset presents the data underlying the interactive map of all training courses accessible via Parcoursup in 2020, 2021, 2022 and 2023 (‘https://dossier.parcoursup.fr/Candidat/carte’). The 2024 data will be completed gradually until 17 January. This dataset is updated daily.

Dataset

This dataset was created by Long Ngg

Released under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

Contents

Maps exist to convey information to people, whether that information is how to get from one point to another or how many oil fields are located in a given region. Effective cartography can convey that information efficiently to map users.In this course, you will be introduced to a five-step workflow for designing and creating maps. This workflow can be applied to any map or output medium (print or digital). This course will cover all steps of the workflow in general terms, emphasizing the first two steps: the cartographic planning process and data evaluation.After completing this course, you will be able to perform the following tasks:Identify and describe the cartographic workflow steps.Explain cartographic design controls and how they drive map creation.Apply the planning step of the cartographic workflow.Evaluate data sources to determine applicability.Discuss why basemap and operational layers are important.Assign the correct coordinate system to data based on the geographic extent and map objective.Assess the level of detail required for a map and apply generalization techniques when appropriate.

These data were compiled for the use of training natural feature machine learning (GeoAI) detection and delineation. The natural feature classes include the Geographic Names Information System (GNIS) feature types Basins, Bays, Bends, Craters, Gaps, Guts, Islands, Lakes, Ridges and Valleys, and are an areal representation of those GNIS point features. Features were produced using heads-up digitizing from 2018 to 2019 by Dr. Sam Arundel's team at the U.S. Geological Survey, Center of Excellence for Geospatial Information Science, Rolla, Missouri, USA, and Dr. Wenwen Li's team in the School of Geographical Sciences at Arizona State University, Tempe, Arizona, USA.

Mapping between the Course-IDs and the corresponding Organization-IDs in WS01

Materials created by James Baker in June 2014 for the 108 Mapping Data course of the British Library Digital Scholarship Training Programme.

Mapping between the Course-IDs and the corresponding Organization-IDs in WS12

Mapping between the Course-IDs and the corresponding Organization-IDs in SS09

We have generated the training and validation labels for the paddy rice mapping. Both the Landsat images and their corresponding masks were rotated by 5°. Subsequently, all the Landsat images and masks were cropped into several small images with dimensions of 256 × 256. These small images covered the Landsat images completely without any overlap, and any cropped image without paddy rice pixels was removed. The training and validation sets for Landsat 5 images were 29906 and 9968, respectively, the training and validation sets for Landsat 8/9 images were 50956 and 16985, respectively.

Mapping between the Course-IDs and the corresponding Organization-IDs in SS04

This dataset was compiled as part of the TIME4CS project, WP4, and lists identified citizen science training resources, as of July 2022.

The EU-citizen.science platform provided the basis for mapping CS training in Europe, as the team behind the platform has put considerable effort into compiling, and encouraging the CS community to contribute, CS training resources. Additionally, training courses were identified based on the case studies in WP1, as most universities do not list their courses on the EU-citizen.science platform.

Summary

This is the extent dataset proposed in the paper "OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation". OpenVid-1M is a high-quality text-to-video dataset designed for research institutions to enhance video quality, featuring high aesthetics, clarity, and resolution. It can be used for direct training or as a quality tuning complement to other video datasets. New Feature: Video-ZIP mapping files now available for efficient video lookup (see Dataset… See the full description on the dataset page: https://huggingface.co/datasets/phil329/OpenVid-1M-mapping.

Quivira National Wildlife Refuge was established in 1955, and a detailed vegetation map was not available for management purposes. With the present development of a biological program and Comprehensive Conservation Plan (CCP), a baseline vegetation map of the refuge was identified as a necessity. Development of the vegetation map and associated report was a multi-step process. Aerial photography (NAIP, 2008) was used with eCognition to create polygons of different plant communities based on the likeness of surrounding pixels in the area. Prior to ground-truthing, the following activities were accomplished: training on vegetation mapping using GIS (previous experience and National Conservation Training Center course), creation of an vegetation association and alliance dichotomous key, development of a refuge plant key and identification skills, and preparation of maps for ground truthing. Once out in the field dominant plants were identified for appropriate vegetation alliance and association classification, plant specimens were collected for the refuge herbarium as necessary and additional observations and photos were gathered for the report. Over the course of the project, classification data was entered into a GIS and polygons were appropriately modified to create the final map. At Quivira, results found a total of 42 alliances and 43 associations.The most dominant plants throughout the refuge in 2008 based on canopy cover were saltgrass, plum, little bluestem and cottonwood. The number of alliances and associations found on the refuge show high species diversity.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles.

The vegetation map was developed by photographic interpretation of 1993, 1:16,000 scale color infrared photography. Two separate classification systems were used to develop the mapping units. Cultural, disturbed, or unsampled vegetation types used the Anderson Level II classification system. All other vegetation within the mapping boundary used map units derived from the NVCS. A total of 8 Anderson Level II classes and 9 NVCS classes were used. The NVCS classes were combined to form 4 vegetation mapping classes. As part of the mapping effort, we have included an accuracy assessment for the overall mapping effort as well as for individual class accuracies. These data include reporting for both errors of omission and commission. Overall map accuracy is 74.3% within a 90% confidence interval. All digital files were created with a standard format. All files are delivered with a UTM projection, zone 13, and a north American datum of 1983.

In this research, the best management practices include vegetative/structural conservation practices (SCP) across crop fields, such as grassed waterways and terraces. This reference dataset includes 500,000 pair patches (false-color image (B1: NIR, B2: Red, B3: Green) and binary label (SCP: yes[1] or no[0]). These training samples were randomly extracted from Iowa BMP project (https://www.gis.iastate.edu/gisf/projects/conservation-practices) and present 90% of patches with SCP areas and 10% of patches non-SCP area. The patch dimension is 256 x 256 pixels at 2-m resolution. Due to the file size, the images were upload in different *.rar files (imagem_0_200k.rar, imagem_200_400k.rar, imagem_400_500k.rar), and the user should download all and merge them in the same folder. The corresponding labels are all in "class_bin.rar" file.

Application: These pair images are useful for conservation practitioners interested in the classification of vegetative/structural SCPs using deep-learning semantic segmentation methods.

Further information will be available in future.

GIS project files and imagery data required to complete the Introduction to Planetary Image Analysis and Geologic Mapping in ArcGIS Pro tutorial. These data cover the area in and around Jezero crater, Mars.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. An ArcInfo (copyright ESRI) GIS database was designed for THRO using the National Park GIS Database Design, Layout, and Procedures created by RSGIG. This was created through Arc Macro Language (AML) scripts that helped automate the transfer process and ensure that all spatial and attribute data was consistent and stored properly. Actual transfer of information from the interpreted aerial photographs to a digital, geo-referenced format involved two techniques, scanning (for the vegetation classes) and on-screen digitizing (for the land-use classes). Transferred information used to create vegetation polygon coverages and linear coverages in ArcInfo were based on quarter-quad borders. Attribute information including vegetation map unit, location, and aerial photo number was subsequently entered for all polygons. In addition, the spatial database has an FGDC-compliant metadata file.