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YolGol The Oriented Imagery Catalog has been deprecated and will be retired relatively soon. https://community.esri.com/t5/oriented-imagery-blog/announcing-deprecation-of-oriented-imagery-quot/ba-p/1578015 Have you read about the version that is integrated into ArcGIS, now referred to as Oriented Imagery Datasets (if used locally) or Oriented Imagery Layers (when published to ArcGIS Online or Enterprise)? For the integrated version, the input metadata table is documented here: https://pro.arcgis.com/en/pro-app/latest/help/data/imagery/oriented-imagery-table.htm The images can remain in their original folder as long as you're working locally. If you're intending to share the OI Layer via the web, the images must be in a web accessible location (http address) and the path in the OI table must be correct. Cody B
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07-31-2025
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Bruce could you please start a new post and tell us clearly and completely what you need help with? ArcGIS Flight does not create orthomosaics so I assume you must be asking about Site Scan or Drone2Map? The original post above referred to processing multiple separate drone flights into orthomosaics, and then seeking to combine the orthomosaics. Is that what you're seeking? You mention "distortion" but did not provide any details (screenshots?) of what you are seeing. Cody
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06-08-2025
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A new version of ArcGIS Drone2Map is now available in MyEsri, and we encourage everyone to upgrade. This release includes a number of improvements to the overall processing, providing increased quality in True Orthos, reduced processing time, and reduced file sizes for the 3D products (published to the ArcGIS Online/Enterprise, or used locally). For users with multispectral sensors, you can now create True Orthos! And if your projects include water bodies that have presented a challenge in the past, we think you’ll love the simpler water body mask available in this new version. 5 band multispectral imagery, showing different band combinations. Imagery courtesy of USGS These are just some of the highlights for the latest version of Drone2Map. You can see more detail in the What’s New blog, and check out our What's New video to see these new enhancements! You can find a complete listing of new features in the online Help, including a list of specific issues which have been addressed.
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05-16-2025
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HasKan You need to provide more context and details to enable people to help. What ArcGIS data, stored where, shared by whom? ArcGIS Online Living Atlas? Community shared data? Your own data? Image Server? Enterprise? Cody B.
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05-16-2025
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hello Matt 1) re: the "source/derived" model, be SURE you use the "Table" raster type when merging source MDs into the Derived. If you use "raster dataset" it can cause performance problems. 2) re: overviews, there's no single answer (depends on # datasets, display scales etc.) but in your case I would probably recommend overviews on the source MDs but not on the derived. That is, unless you're sure your users *always* want to see the drone imagery on top, in which case you could also create overviews on the derived MD. You'd need to rebuild them every time you add a new drone mosaic to the drone MD - that would be true for the drone MD as well as the derived composite mosaic, if you *do* want them on the derived MD. 3) when you said "My source drone MD currently doesn't have overviews and also displays fine unless you zoom out to 1:36,112. At which point, it disappears", creating overviews on that source MD should fix that. 4) You may already know, but the MinPS and MaxPS fields in the MD control when the associated layer is made visible. https://pro.arcgis.com/en/pro-app/latest/help/data/imagery/mosaic-dataset-attribute-table-pro-.htm and if you read the section on "Cell Sizes" in this doc https://pro.arcgis.com/en/pro-app/latest/help/data/imagery/source-derived-and-referenced-mosaic-datasets.htm you'll find some advice. Note that with mosaic datasets of mixed sources and resolutions, the automated tool (Calculate Cell Size Ranges) isn't always able to make the right decisions, so you may have to manually decide the desired visibility of each drone mosaic (? all the same?) and set them manually in the Source MD. If you use the Table raster type, the MinMax PS should be copied over. 5) Last, if you want to use the MDCS scripts to make this automated and repeatable (recommended), the "Preprocessed orthos" example fits your situation. See https://www.esriurl.com/preprocessedorthos and look for the Try It Out tab Cody
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04-20-2025
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It's difficult to advise without seeing your configuration, but here are some suggestions: 1) if you aren't familiar with https://esriurl.com/imageryWorkflows , that site provides a depth of knowledge I advise for anyone working with imagery in ArcGIS 2) I'd recommend you implement the Source/Derived mosaic dataset model to maintain your imagery over the long term. The base layer of 2024 aerials would be in one source MD, and you can add new drone flights into a "drone update" MD. If you have multitemporal drone flights over the same site it would take additional configuration advice, but if you want all the drone flights in the "new/on top" layer it should work well. 3) If you'll be doing ongoing updates I'd strongly encourage you to use the configurable scripts to (semi)automate this process. That would enable you to rebuild your mosaics easily if anything got corrupted or misconfigured. 4) I'd recommend that you add a new field into the MD's to define which you want on top - in the ImageryWorkflows we talk about a "best" field but you may choose a different field name. You'd use Mosaic Method = By Attribute and point to that ("best") field in the Derived mosaic, then ensure that your "best" field values are calculated to ensure lower numbers for images to appear on top. In simplest terms, the base layer aerials could be "best=2" and drone layer "best=1" but if you want to order everything by date, you could calculate "best = (January 1, 2999) - acquisition_date" to ensure newer datasets have lower values. (This may be confusing - in this last statement I'm assuming you'd have a different "best" value for every record in the drone mosaic) One other comment - although the mosaic dataset and image service are a dynamic data structure (can change which image is on top) remember the overviews are static - they show the state of the mosaic (at the scales they cover) when you create them. Thus overviews are appropriate on the source MD for the base aerials layer, and depending on the size of your individual drone mosaics you may want to rebuild overviews on the drone mosaic layer each time you add a new drone flight, then just ensure the drone mosaic has a "best" value lower than the base aerials mosaic as referenced within the Derived MD. If any of that isn't clear, let me know. Note the "Source/Derived mosaic dataset model" is a specific configuration with important methods - if you're not familiar with it, review the workflows site before proceeding. Cody
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04-18-2025
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Kph_1818 Esri has several options for processing drone imagery - http://esri.com/drone2map (mentioned above) will run on your laptop. Or, if you'd prefer to process in the cloud, you should consider Site Scan (see https://www.esri.com/en-us/arcgis/products/arcgis-reality/products/site-scan-for-arcgis) Both of these are able to process imagery captured by DJI drones, and you can use the DJI flight controller (if that is what you mean by "the DJI drone flying pattern"?) but you can also use Esri's free drone flight control app, http://esriurl.com/ArcGISflight which helps ensure good quality image capture in the field. Let us know if you have more questions Cody B.
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04-06-2025
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Giuseppe it is hard for us to advise if we don't fully understand your configuration. The mosaic dataset can be used for a wide range of configurations, e.g. to mosaic multispectral imagery from multiple satellites across the entire world, or to manage multiple years of preprocessed orthos covering the same region, with most recent appearing on top, or manage and share multiresolution elevation datasets (or many other examples). I would encourage you to spend some time reading http://esriurl.com/imageryWorkflows and specifically the many options in https://doc.arcgis.com/en/imagery/workflows/resources/using-mosaic-datasets-to-manage-imagery.htm I would first make a zipped copy of your geodatabase just in case something goes wrong. If you break the MD you can delete it and restore your original. You cannot "overwrite" images in a mosaic dataset - every image is a unique record, with path and filename to the source image. You can remove images by selecting them in the attribute table and then right click on the MD in Catalog and select "Remove Rasters" You should also delete the overviews if the images you're removing contribute to them (If you have a nationwide mosaic and you're only removing images for one small region, you may not need to delete all overviews) Then you can add the new rasters, presumably build footprints for them (unless they're simple tiles aligned with the SRS of the MD), make sure the new images are appearing at the right scales, and then rebuild overviews. But my advice may be too simple depending on what else is in your mosaic dataset, and how you have its properties configured. See the URLs above for more information. Cody
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03-23-2025
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To KNTR - this does not answer your request (Randall already explained) but I wanted you to know the Experience Builder widget is now available. New Experience Builder (ExB) widget for Oriented Imagery is available!
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03-02-2025
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For those of you who've been using (or wanting to use) oriented imagery in the web, and you'd been waiting for the ability to configure a web app for your organization, the new Oriented Imagery Viewer widget for Experience Builder is here! (Released today, Feb 26, 2025) With this widget, organizations can now build flexible web apps to maximize the value of their oriented images. The Experience Builder apps can load and choose from multiple oriented image layers and/or display two or more separate oriented image windows. If you need to make measurements, horizontal distances and areas on the ground are enabled via this widget, as well as vertical height measurements. Accuracy estimates are automatically included (based on metadata). And working with GIS features is now enhanced: feature data can be displayed in the viewer, and new features can be digitized directly within the oriented images. Build your apps, and let us see what you've accomplished!
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02-26-2025
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Morgan - thanks, knowing those were completely different dates does make the color correction more challenging. Can you clarify terminology and workflow? You said "mosaic dataset" and I just want to clarify how you're using them. https://pro.arcgis.com/en/pro-app/latest/help/data/imagery/mosaic-datasets.htm Are you processing date 1 in Drone2Map, then a separate project for date 2 (etc.), creating multiple true orthos for different sections of campus, then managing those orthos using a mosaic dataset? Presumably intending to create a raster tile cache (basemap) out of the mosaic dataset when campus is complete? If correct, this is a perfectly valid workflow for the geometry of the pixels, but unfortunately color correction will be much more difficult because each true ortho is adjusted for color, but the edges will be impossible to match. I'd suggest one of three alternatives to improve the tonal mismatch between orthos: 1) fastest/easiest is to apply blending across the seamlines between the true orthos, and you should be able to apply a wide blend distance (? 10 meters?) if you have adequate overlap. (And for future flights, ensure a lot of overlap between the output orthos, e.g. 2 full flightlines if possible). This should look relatively good at large scale (zoomed way in) but when you zoom out, you'll still see tonal differences between the flights. 2) another option is to process all drone images in a single Drone2Map project - this should automatically compensate for the tonal differences but depending on total number of images the project may be too large. (Do you have access to the ArcGIS Reality Extension for Pro? That should allow you to scale to a larger single project) 3) I was going to describe a third option but I'm pretty sure this will not yield acceptable results. You could use Drone2Map flight-by-flight to run photogrammetric processing but not create true orthos, then apply the exterior orientation (xyz, omega phi kappa) for all of the individual frames using a mosaic dataset to rectify the images on-the-fly; but this won't give you a true ortho, and with a 200' flight over campus buildings the horizontal layover will be unacceptable. (if you ever had to do a large project over natural terrain, this approach might be perfectly acceptable) Cody
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02-26-2025
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That "Road" result does not look normal - you should not see sharp edges from different images. Can you confirm which version of Drone2Map you're using, and the settings you applied?
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02-23-2025
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Tim Sorry this hasn't been answered yet. You should be able to process the original RJPG files without conversion, although you're correct that we have not implemented any corrections for ambient conditions or distance to sensor. I'd be curious to discuss your requirements. (I sent you a direct message) One detail to note, for DJI thermal sensors only, is software from DJI is required to perform the conversion from raw digital numbers (proportional to energy) to temperature units. We don't install that software by default due to the security limitations I assume you've heard about. This doesn't impact geometric processing, so it's not required to run a project, but you won't get temperature units - I think that may be what you indicated above. If you don't have the DJI download, check your MyEsri portal - let us know if you can't find it. Also note you can choose Celsius or Fahrenheit but you have to make that choice before processing. Cody B.
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02-10-2025
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Introduction The use of thermal infrared (TIR) imagery sensors has seen dramatic growth in the last few decades, addressing many applications for users of mapping and GIS. The purpose of this blog is to introduce some of the many use cases, and to provide recommendations for ArcGIS users to take advantage of these sensors. The examples included here will focus on imagery captured from an aerial drone followed by photogrammetric processing to generate a True Ortho composite image, but use of a drone and post-flight processing are not always required - some very powerful applications may also be addressed with single images or video (airborne or ground-based), and for large area studies, satellite sensors may be most appropriate. Note that we’re using the acronym “TIR” here to refer to the thermal infrared region of the electromagnetic spectrum. This blog does not address properties of near infrared (“NIR”) or shortwave infrared (“SWIR”) energy or sensors. Use Cases Some of the many applications for TIR imagery within ArcGIS include: 1. Monitoring energy loss from buildings This application is growing rapidly since it provides a low-cost analytical method for a facility to reduce operational costs as well as environmental impact. Thermal infrared imagery is typically captured by a drone to view the rooftops of buildings under study, and areas that are losing energy can be easily observed as warmer or cooler than their surroundings. That is, if the outdoor weather is warm and the building has air conditioning, energy leaks will appear as cool regions. If the building is losing interior heat during wintertime, relative warm areas may be visible. Areas of the building that are noticeably cooler or warmer than their surroundings help building managers prioritize improvements to ventilation systems as well as locate and repair areas of insufficient building insulation. Examples can be found in this publication from ACCESSiFLY. An example image (showing false colors applied to the single band TIR image) is shown here, highlighting warm regions around roof vents. 2. Detecting potential maintenance problems in electrical/mechanical equipment Another application where TIR imagery can provide value is by identifying equipment that appears to be overheating, indicating possible physical wear or failing electronics. This is applicable for a variety of types of equipment, including but not limited to photovoltaic solar arrays, electrical transformers, industrial motors, and more. An example image is shown below, provided courtesy of Rocky Mountain Unmanned Systems. In this image, one section of the photovoltaic (PV) array is noticeably brighter (warmer) than the neighboring panels in the installation, indicating a possible defect in either the array or its control electronics. With prompt maintenance, the operator can reduce lost productivity and possibly avoid a more serious failure in the future. 3. Detecting leaks in pipelines Thermal Infrared imagery is also applied to monitoring pressurized pipelines to detect leaks. A leak can sometimes be observed as an area on or near the pipeline with a temperature anomaly – warmer or cooler than the surrounding region. If a liquid such as water is leaking, it can cool the local vegetation/soil relative to ambient background temperature. In the case of other pipelines, such as natural gas, a leak can result in a loss of pressure, and evidence of emerging gas can sometimes be observed as a cold plume (although there are other sensors that are better suited for detecting flammable gas). In the example images below (provided courtesy of EagleHawk) we can clearly see the route of an underground steam pipe (1 in the first image, TIR), with evidence of a condensate leak (2) entering a storm drain. In top center (3) we can see the warm water condensate running into a nearby ravine. The second image provides a natural color view of the same site. Based on this imagery, the leak was promptly located and corrected. 4. Other Use Cases The use cases listed above are just a sample of the much broader array of applications for thermal infrared imagery. There are many more – for example: TIR sensors are used on drones as well as crewed aircraft to search for lost hikers or conduct wildlife surveys. In law enforcement, residual heat can be detected and applied as forensic evidence showing where a vehicle had been parked but then departed the scene prior to image capture – either where heat from an operating vehicle had warmed the pavement, or alternatively where the shadow of a parked vehicle had left evidence as a cooler region, having reduced solar heating of the surface. Environmental studies focusing on aquatic habitats where narrow temperature ranges are required for successful fish spawning can cover relatively large areas with thermal infrared imagery to supplement in situ temperature sampling. Interpretation / Analysis For most of the above examples, users can detect thermal anomalies through simple visual assessments of the area of interest to identify regions that appear warmer (or colder) than everything nearby. As a result, the user can extract useful information with relative (qualitative) observations, but without performing absolute (quantitative) measurements of exact temperatures. The exception to this in the above examples is an application such as monitoring water temperatures in fish spawning beds, where spawning success may depend on water temperatures within a relatively narrow temperature range. In use cases that require absolute and accurate temperature measurements, users will need to consider radiometric calibration of their sensor system, and properties of the materials being observed, especially metal surfaces which often show reflections of TIR energy coming from another source – e.g. the sky (which will typically appear very cold) or the sun. Surface Properties and Emissivity Making accurate remote temperature measurements of an object is challenging. Images captured by thermal infrared sensors are proportional to the energy received, and those energy measurements are then used to determine the apparent temperature of the surfaces in view. Some sensor manufacturers provide information to enable conversion of TIR images into energy units, then from energy to surface temperature. An important consideration is the surface property referred to as Emissivity (represented by ε). Different surface materials emit thermal energy at different rates. The difference in emitted energy for two surfaces at the same temperature is characterized by their emissivity, ranging from 0.0 to 1.0. The apparent temperature of a surface is typically lower than the actual temperature, presuming the surface emissivity is less than 1.0. Remote temperature measurements can be misleading for low ε surfaces such as most metals, since they emit very little energy and they can also reflect TIR energy coming from another source (e.g. the sky, which will appear to be very cold, or a separate heat source). A more detailed discussion of this topic is beyond the scope of this blog post, but it is important to understand that calculation of the most accurate temperature of any surface – concrete, water, grass, metal, etc. – would require knowledge of the emissivity of that surface as well as the possibility of observing reflected heat sources. A table of emissivity values can be found HERE. Note that, since most natural surfaces have an emissivity value of 0.9 or greater, in most applications described above, correction for emissivity may not be necessary. Practical Application In practice, the user should consider whether absolute temperature measurements are required for their use case. Since many applications do not require absolute temperatures, it is reasonable to visually identify thermal anomalies, and thus derive significant value from TIR imagery without needing to calibrate to temperature units. For TIR sensors carried on a drone, users wishing to generate a True Ortho data product must capture multiple frames (with recommended overlap of 90% due to the low image resolution) and then run photogrammetric processing to create an orthomosaic for visual interpretation. For satellite sensors, a single image will often cover the full site of interest. If absolute temperature measurements are required, support in Esri software will depend on the sensor. Additional software from the sensor manufacturer may be required to perform the conversion. Refer to the ArcGIS Help documentation for your software and sensor. The sensor manufacturer may also have separate tools to apply for temperature calibration. For satellite sensors, the proper Raster Type must be defined, then processing templates can be applied to the TIR bands to convert pixel values into degrees Celsius or Fahrenheit
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02-07-2025
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Oriented Imagery Classic — the add-in, community-supported solution for managing and visualizing street-level images, 360 images, full-resolution drone images, video, mobile phone images, and more — is being deprecated. We are integrating the Oriented Imagery capability directly into the ArcGIS system. An introduction is available here. The integrated version is replacing Oriented Imagery Classic. Oriented Imagery Classic will be maintained until the integrated capability within ArcGIS has reached equivalency. This deprecation notice is offered to ensure users have sufficient time to migrate their data and workflows to the new data model and tools, at which point Oriented Imagery Classic will be retired. Please visit the deprecation notice on the Esri Support Site for more information.
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01-22-2025
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