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How to improve the dense point clouds automatic classification using points selection by color and points selection by shapes tool in Agisoft Metashape.   Keyframe: 00:00 Point Clouds Selection by Color Method 04:51 Point Clouds Selection by Shapes Method

Here are volume 2 of the Agisoft Metashape Tricks for Optimal Photogrammetric Processing Workflow.  In this video, I demonstrate again 3 tricks that maybe would be useful for you out there.  The topics are as follow:  1 Export Orthomosaic with enhanced brightness and contrast 2 Camera Optimization Settings for DJI Phantom 4 Pro/Standard 3 Camera Optimization Settings for DJI Phantom 4 RTK

Here are volume 2 of the Agisoft Metashape Tricks for Optimal Photogrammetric Processing Workflow.  In this video I demonstrate again 5 tricks which maybe would be useful for you out there.  The topics as follow:  1 Set DSM and DSM as separated instance layer 2 Checking if the Images are pure not aligned or not aligned as well as not calibrated 3 Filtering Dense Point Clouds based on Confidence Score 4 Orthomosaics Seamline Refinement for a better visual looks 5 Orthomosaics Color Balancing and Calibration

Here are 5 tricks for your photogrammetric workflow in Agisoft Metashape.  00:00 : Key Settings for Stereo / Tri Stereo Satellite Imagery Processing 00:57 : Dealing With 16 BIT Images 01:28 : Measure Image Quality 02:19 : Rolling Shutter Compensation 03:14 : Filtering Sparse Point Clouds More tricks are incoming

How to perform Post Processing Kinematic GNSS of Phantom 4 RTK Photogrammetric survey data to obtain centimeters grade accuracy, using Open Source RTKLIB Software. 

How to perform Post Positioning Kinematic GNSS Processing of Phantom 4 RTK Photogrammetric survey data to obtain centimeters grade accuracy, using ASP-SUITE Software.  In this video, I would like to show you how to perform Post Processed Kinematic (PPK) GNSS of Phantom 4 RTK UAV Photogrammetric Survey Data. I will make at least two videos about this topic. The first video is about how to do PPK using ASP Suite Software. ASP Suite is a commercial software dedicated to doing this kind of processing. The second video is about how to do PPK using RTKLibs. RTKLibs maybe be a better solution because it is open-sourced so the software would be easily reached by many users, so stay tuned if you are excited about this topic. Besides ASP Suite and RTKLibs, there is some other software that has abilities to do this kind of processing, but I will limit the tutorial to focus on these two.  Now let's go first with ASP Suite

How to perform vector geospatial data georeferencing in ArcGIS Desktop. This georeferencing procedures can be applied to CAD Data like DWG or DXF or any other vector data format, so the result can be used for GIS analysis and mapping. 

Second part of the tutorial series about Automatic Road Extraction From Satellite Imagery or Aerial Photographs using ArcGIS Pro. This video will demonstrate about how to build training data, train the deep-learning model and use the trained model for inferencing road network on satellite imagery and aerial photographs. 

Automatic Road Extraction From Aerial Photographs/Satellite Imagery Using ArcGIS Pro Part 1.  Apply the finished model to extract the road network in raster format.  This video is the first part of the few videos about how to extract road network from satellite imagery / aerial photographs in ArcGIS Pro.  Ready to use DLPK model can be downloaded from : https://www.arcgis.com/home/item.html?id=0c00be3c7e4042ebadd3ae1404190a5b

How to get a fine and detailed base map or online imagery base map in your ArcGIS Desktop or ArcGIS Pro Layout. This trick will enable you not to be too dependent on a physical imagery file which usually has a large size and usually takes a decent amount of processing time or hardware resources. This trick will speed up your cartographic production and saves you a ton of time. 

A little trick in ArcGIS Desktop about how to create polygon envelopes, extents, or rectangles straight from a stand-alone table in ArcGIS Desktop. This tool will be very useful for those who manage large collections of scanned maps or imagery, or for those who are working with spatial data catalogs.

How to visualize water-body features to be close-to-reality (water body has reflections and animated ripples) in 3D view. This technique would be very useful for 3D GIS visualization and digital twin creation. 

Akhirnya Datum SRGI 2013 (Sistem Referensi Geospasial Indonesia) terimplementasi di ArcGIS Pro dan QGIS. Informasi proyeksinya dalam format PRJ juga dapat di-import di ArcGIS Desktop. SRGI 2013 di ArcGIS dan QGIS terimplementasi sebagai proyeksi geosentris dan proyeksi UTM dengan Zona mengikuti Zona UTM pada umumnya. Adanya parameter SRGI 2013 di software GIS terkemuka memungkinkan untuk transformasi koordinat pada level data dapat lebih mudah dilakukan di dalam lingkungan software GIS. 

How to realistically visualize Tree and Vegetation in 3D Mode using ArcGIS Pro. You can use point data mode or polygon data mode for this kind of visualization.  Collection of ESRI pre-made Rule Package file (RPK) for your 3D Modeling: https://www.arcgis.com/home/search.html?q=RPK

How to generate 3D texturization and realistic visualization in ArcGIS Pro using Open Street Map Building Data. Stay tuned for more tips and tricks about how to display geospatial data in 3D realistic mode, to reach the geospatial digital twin.

How to generate RGB composites of Multi-temporal SAR Radar Imagery in ESA SNAP. Multi-temporal SAR Imagery is very useful to monitor the landuse and landcover dynamic over time. This data could also be useful for Image Classification or Image Segmentation from SAR Data. RGB composites of SAR data are not the same as RGB composites of Optical Imagery Data. The color information of SAR Color composite doesn't represent the electromagnetic response of land cover. It is more represents the gradual changes of radar backscatter response at different times.

SAR imagery is distorted geometrically and radiometrically In mountainous and hilly areas,. With Geometric Terrain Correction and Terrain Flattening applications, both the geometric and radiometric terrain effects can be removed from the backscatters, leaving noise being the only factor affecting the land cover classification accuracy. A multi-temporal compositing technique in ESA-SNAP can be applied in this case to reduce the noise level. Also, spatially varying local resolution effects can be compensated if images of both ascending and descending views are combined (not performed in this video but you can try it by yourself). The technique described in this video generates a composite SAR image from a stack of terrain-flattened and geometrically terrain corrected images together with their simulated local illuminated areas. 

How to build 3D GIS and Geospatial Data from Level of Detail 0 (polygon) to Level of Detail 1 (extrude model and multipatch) using ortho imagery and digital elevation raster obtained from UAV / Drone Photogrammetric Processing.  

How to extract building footprints polygon from point clouds data generated from LIDAR or Photogrammetric UAV Survey. If you have LIDAR points clouds, the perfect and precise result is guaranteed. If you are using Point clouds data generated from photogrammetric processing of aerial or UAV Survey, make sure you perform precise point clouds classification first, or else some misclassified and wrongly extracted building footprints are expected. 

Image classification for landcover mapping has so many types of algorithms or methods. One of them is Image Segmentation and Classification. The process can be described as imagery is segmented into many segments, and from there we classify them according to the specified classification schema.  This video tutorial below is about how to perform object-based image segmentation and classification using Satellite Imagery data. Not only the segmentation, but I also made this video as a workflow, which means I demonstrate from the training samples generation (for supervised classification), ground truth creation data to support accuracy assessment, the image segmentation process, and closing with the accuracy assessment to inspect the accuracy of the final classified raster. Almost every step is automatic so it will save a ton of time and effort.  So if you are curious, check this below

In this tutorial, I will show you a new application from ArcGIS Enterprise/Portal for ArcGIS Family. The application is called Ortho Maker. This app is designed to provide Photogrammetric UAV Photo processing inside ArcGIS  Enterprise/Portal. If you are familiar with DroneDeploy or Pix4D Clouds, this application offers similar features.  Because it is implemented inside ArcGIS Enterprise Environment, you can publish the processing results as ArcGIS API Services, either in tiled static map services or dynamic image services. Previously, ESRI has released Drone2Map as a desktop-based UAV Processing software a few years ago, and UAV photogrammetric processing capabilities also have been implemented in ArcGIS Desktop and ArcGIS Pro. So Ortho Maker is the ESRI answer for Cloud-based UAV Photogrammetric processing needs.  Ortho Maker implementation needs an instance of ArcGIS Image Server (functioned as raster analysis module and raster hosting server). But for optimal performance, ESRI reco

Definisi Sistem Informasi Geografis Sistem Informasi Geografis (Geographic Information System/GIS) yang selanjutnya akan disebut SIG merupakan sistem informasi berbasis komputer yang digunakan untuk mengolah dan menyimpan data atau informasi geografis (Aronoff, 1989). Secara umum pengertian SIG sebagai berikut: ” Suatu komponen yang terdiri dari perangkat keras, perangkat lunak, data geografis dan sumberdaya manusia yang bekerja bersama secara efektif untuk memasukan, menyimpan, memperbaiki, memperbaharui, mengelola, memanipulasi, mengintegrasikan, menganalisa dan menampilkan data dalam suatu informasi berbasis geografis ”. Data Spasial Sebagian besar data yang akan ditangani dalam SIG merupakan data spasial yaitu sebuah data yang berorientasi geografis, memiliki sistem koordinat tertentu sebagai dasar referensinya dan mempunyai dua bagian penting yang membuatnya berbeda dari data lain, yaitu informasi lokasi (spasial) dan informasi deskriptif (attribute) yang dijelaskan berikut ini :

Aplikasi ArcGIS sebenarnya terdiri dari beberapa aplikasi dasar yaitu: ArcMap, ArcCatalog, ArcToolbox, ArcScene dan ArcGlobe. 1. ArcMap merupakan aplikasi utama yang digunakan untuk mengolah, membuat, menampilkan, memilih, editing dan layout peta. 2. ArcCatalog merupakan aplikasi yang berfungsi untuk mengatur berbagai macam data spasial dalam ArcMap, meliputi fungsi browsing, organizing, distributing, deleting data spasial. 3. ArcToolbox merupakan aplikasi perangkat/tools dalam melakukan analisis-analisis geospasial. 4. ArcScene merupakan aplikasi mengolah dan menampilkan peta-peta ke dalam bentuk 3D 5. ArcGlobe merupakan aplikasi yang berfungsi untuk menampilkan peta-peta 3D ke dalam bola dunia dan dapat dikoneksikan langsung dengan internet. ArcCatalog ArcCatalog adalah salah satu program dari ArcGIS yang bisa digunakan antara lain untuk menelusuri atau mencari data (browsing), mengorganisir (organizing), mendistribusikan (distributing) dan mendokumentasikan (documenting) suatu st

This is the newest satellite imagery cloud removal and correction tutorial I have made. The workflow described in this tutorial is similar to my previous tutorial about satellite imagery clouds removal and correction using ArcGIS Desktop. The only difference is, the workflow I described in this video is better. Because this time, we could use the QA Band as the reference cloud mask. So, it is more simple, robust, accurate, precise, and guarantees a better result. It is all thanks to Graphical Raster Functions Editor implemented in ArcGIS Pro. 

Satellite Imagery Orthorectification usually uses interior/exterior orientation information for the photogrammetric collinear equation to works. Satellite Imagery vendors usually gave this information to the users in a form of a sequence of constant numbers written in a certain format and can be consumed by photogrammetry software. This information is called Rational Polynomial Coefficients (RPC) and is usually provided along with other imagery metadata by satellite imagery companies.  This RPC information became the backbone of orthorectification. Without this RPC information, orthorectification can't be done and can't be performed. Fortunately, some remote sensing software can compute this RPC information from the Sensor Characteristics information and it can also be more accurate if complemented by Ground Control Points measured in the field or measured from more accurate imagery.  I will explain how to do this later. In this article, I will show you how to perform orthorect

Numerous Pan-sharpening Algorithm has been developed. Some of them are better in preserve the spatial details of the panchromatic band but have poor spectral information preservation of multispectral bands, and vice versa. ArcGIS has implemented some of the most well-known Pan-Sharpening algorithms into its roster of geospatial tools. Some are good, some are good only in certain aspects.  Fortunately, Pan Sharpening implementation in ArcGIS also complemented with a weighting factor. These weights value can be derived from the data-driven approach. By using these weights, we can further control the Pan-Sharpening algorithm that has poor ability to maintain spectral information like IHS to be more consistent with the multispectral bands. And this is what the video tutorial below is about. 

Satellite Imagery Orthorectification usually uses interior/exterior orientation information for the photogrammetric collinear equation to works. Satellite Imagery vendors usually gave this information to the users in a form of a sequence of constant numbers written in a certain format and can be consumed by photogrammetry software. This information is called Rational Polynomial Coefficients (RPC) and usually provided along with other imagery metadata by satellite imagery companies.  This RPC information became the backbone of orthorectification. Without this RPC information, orthorectification can't be done and can't be performed. Fortunately, some of the remote sensing software has capabilities to compute this RPC information from the Sensor Characteristics information and complemented by Ground Control Points measured in the field or more accurate imagery.  I will explain how to do this later. In this article, I will show you how to perform orthorectification using custom or

When building RPCs for digital camera aerial photography and pushbroom sensor imagery, you will need to enter various required parameters such as   principal points ,   focal lengths and pixel sizes , and   incidence angles . This section provides guidelines on determining these values. Principal Point Coordinates Principal point coordinates are often set to [0.0, 0.0], which assumes that the principal point is the center of the image for a frame central projection and the center of each scan line for a line central projection. A laboratory calibration report should provide the principal point coordinates. Focal Length and Pixel Size Focal length is the orthogonal distance from the perspective center to the image focal plane. Pixel sizes correspond to the CCD cells (detectors of the camera that captured the images). Typically, aerial digital cameras and satellite pushbroom sensors have square pixels, which means that the pixel size is the same in the x and y dimensions. Focal length an

Cloud cover is a long-time problem for optical imagery analysis in remote sensing. It is obscuring important earth surface object that became the main target on image analysis. There are numerous methods and efforts have been developed to fix the cloud cover problem. The solutions are coming from simple approaches like masking and replacing using a raster calculator or map algebra, to more complex ways like multitemporal pixels blending, fast Fourier transform-based filtering, or even machine learning methods.  The correction results also vary. Sometimes certain method could give a nearly perfect cloudless image, but sometimes it doesn't. One simple way to remove cloud in optical imagery is using the masking and replacing method. This method needs at least two imagery covering the same area at different dates, so it is expected that two imagery will have different cloud cover conditions, and finally, both of them can be merged together to create a cloudless image.  This approach ha

WebODM (web Open Drone Map) is a suite of Open Source UAV/Drone-based photogrammetric processing software running on the server-based infrastructure. This software is developed to provides UAV Photogrammetric Mapping Services over the internet using Server-Client architecture and clouds based computing engine. If you are familiar with Pix4D clouds or DroneDeploy, this software offers similar services, only it is free-to-use and open-source licensed. So you can also contribute to the development.  Personally, I have known this software for quite a time but didn't have time yet to learn deeper about it. Recently my some guys in my office have been successfully deploying this software so finally, I can start to use this software more regularly. Basically, this software is on par with its commercial counterparts like Pix4D or DroneDeploy. WebODM also has an API REST Interface that enables us to integrates the services with the geoportal system (either open-sourced like Geoserver/Geonod