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TwitterThis is an open source object detection model by TensorFlow in TensorFlow Lite format. While it is not recommended to use this model in production surveys, it can be useful for demonstration purposes and to get started with smart assistants in ArcGIS Survey123. You are responsible for the use of this model. When using Survey123, it is your responsibility to review and manually correct outputs.This object detection model was trained using the Common Objects in Context (COCO) dataset. COCO is a large-scale object detection dataset that is available for use under the Creative Commons Attribution 4.0 License.The dataset contains 80 object categories and 1.5 million object instances that include people, animals, food items, vehicles, and household items. For a complete list of common objects this model can detect, see Classes.The model can be used in ArcGIS Survey123 to detect common objects in photos that are captured with the Survey123 field app. Using the modelFollow the guide to use the model. You can use this model to detect or redact common objects in images captured with the Survey123 field app. The model must be configured for a survey in Survey123 Connect.Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS tools.InputCamera feed (either low-resolution preview or high-resolution capture).OutputImage with common object detections written to its EXIF metadata or an image with detected objects redacted.Model architectureThis is an open source object detection model by TensorFlow in TensorFlow Lite format with MobileNet architecture. The model is available for use under the Apache License 2.0.Sample resultsHere are a few results from the model.
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TwitterIn this lesson, students are introduced to the idea and practice of “remote sensing” and provided with hands-on, field-based experience using standardized stream habitat assessment protocols (the National Rivers and Streams Assessment [NRSA]), publically available spatial data, and GPS/GIS technology and workflows to develop multi-scale scale profiles of the physical habitat characteristics of a small portion a selected watershed (the exercise can be adapted to focus on any watershed for which high resolution air photos are available). Students will work individually or as a team (e.g. in pairs) to use a Survey123 App adapted from the NRSA protocol for assessing the habitat and physical characteristics – particularly those that have been influenced by human activity – of the riparian area of a stream or river. This lesson requires the instructor to have access to an ArcGIS Online (AGOL) Creator or Editor account and for students to have access to mobile devices with the Survey123 App installed (which will require students to have an AGOL account). A Survey123 App designed for this lesson that can be adapted to suit the instructor’s needs is included in xml format.
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Put your greyhound on the mapDiscover greyhounds as pets all over the world and submit your greyhound's story in this ArcGIS Web Experience.Access: https://tinyurl.com/greyhoundstoriesCreated by Cristina from All That Geo.Data crowdsourced with ArcGIS Survey123.Built with ESRI's ArcGIS Web Experience (includes and ArcGIS Survey123 and two ArcGIS Dashboards) - tutorial available here.
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Crowdsourcing is a way to obtain information from the public by enlisting the services of the general public or volunteers via the Internet. This can be a valuable source of information during a disaster if it is done in a way that answers important questions, for instance:
Where are roads obstructed by standing water?
What homes have individuals that need assistance evacuating?
Where are debris that needs to be removed?
Tutorial Audience: GIS / Technology SpecialistsEnd User Audience: Emergency Management Planning and Operations Staff
Problem: The emergency management agency has requested that you prepare a crowdsourcing solution in advance of the next disaster. The data collected must be structured in a way so that it is informative and actionable for decision makers. Your crowdsourcing app should be:
Fast and easy to use for the Public.
The database behind it should contain structured information that answers specific questions for emergency managers (e.g. Where are the hazards? How severe is it? Where should we focus our resources?).
Allows for submitting attachments such as photos.
Solution: Survey123 Web FormRequirements: You will need a license for ArcGIS Online to complete this tutorial.Note: This tutorial works with the Public Information Application Tutorial.
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TwitterThis lesson will enable students to create and use a survey to capture where their food is coming from. This lesson uses ArcGIS Survey123 and is part of a wider lesson on Food Sustainability accessible here
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TwitterThis is an open source object detection model by TensorFlow in TensorFlow Lite format. While it is not recommended to use this model in production surveys, it can be useful for demonstration purposes and to get started with smart assistants in ArcGIS Survey123. You are responsible for the use of this model. When using Survey123, it is your responsibility to review and manually correct outputs.This object detection model was trained using the Common Objects in Context (COCO) dataset. COCO is a large-scale object detection dataset that is available for use under the Creative Commons Attribution 4.0 License.The dataset contains 80 object categories and 1.5 million object instances that include people, animals, food items, vehicles, and household items. For a complete list of common objects this model can detect, see Classes.The model can be used in ArcGIS Survey123 to detect common objects in photos that are captured with the Survey123 field app. Using the modelFollow the guide to use the model. You can use this model to detect or redact common objects in images captured with the Survey123 field app. The model must be configured for a survey in Survey123 Connect.Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS tools.InputCamera feed (either low-resolution preview or high-resolution capture).OutputImage with common object detections written to its EXIF metadata or an image with detected objects redacted.Model architectureThis is an open source object detection model by TensorFlow in TensorFlow Lite format with MobileNet architecture. The model is available for use under the Apache License 2.0.Sample resultsHere are a few results from the model.