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Sometimes you need to create a satellite navigation tracking device that communicates via a low-power mesh network. [Powerfeatherdev] was in just that situation, and they whipped up a particularly compact solution to do the job. As you might have guessed based on the name of its creator, this build is based around the ESP32-S3 PowerFeather board. The PowerFeather has the benefit of robust power management features, which makes it perfect for a power-sipping project that’s intended to run for a long time. It can even run on solar power and manage battery levels if so desired. The GPS and LoRa gear is all mounted on a secondary “wing” PCB that slots directly on to the PowerFeather like a Arduino shield or Raspberry Pi HAT. The whole assembly is barely larger than a AA battery. It’s basically a super-small GPS tracker that transmits over LoRa, while being optimized for maximum run time on limited power from a small lithium-ion cell. If you’re needing to do some long-duration, low-power tracking task for a project, this might be right up your alley. https://hackaday.com/2024/10/17/tiny-lora-gps-node-relies-on-esp32/

The Role of a GIS Portfolio: More Than Just a Resume A resume provides a snapshot of your education, skills, and experience, but a GIS portfolio offers a deeper dive into what you can actually do. It's the difference between telling and showing. While a resume might list "proficiency in ArcGIS" as a skill, a portfolio can demonstrate this proficiency through detailed examples of projects you've completed, maps you've created, and problems you've solved using GIS technology. Your GIS portfolio should include a variety of work samples that highlight your capabilities across different areas of GIS. This might include: Maps and Visualizations: High-quality maps that demonstrate your ability to analyze spatial data and present it in a clear, compelling manner. Project Descriptions: Detailed write-ups of the projects you've worked on, including the challenges you faced, the solutions you implemented, and the impact of your work. Data Analysis: Examples of your ability to analyze and interpret spatial data, using tools such as ArcGIS, or other GIS software. Programming and Automation: If applicable, include scripts or code snippets that show your ability to automate GIS tasks or perform advanced spatial analysis. By including these elements, your portfolio becomes a powerful tool that not only highlights your technical skills but also tells the story of your professional journey in GIS. Building Your Portfolio: A Step-by-Step Guide Creating a GIS portfolio might seem daunting, especially if you're early in your career and don't yet have a wealth of experience to draw from. However, with a strategic approach, you can build a portfolio that effectively showcases your potential. 1) Start with What You Have Don't wait until you've accumulated years of experience before you start building your portfolio. Start with the projects you've completed during your education or any internships you've done. Even classroom assignments can be valuable portfolio pieces if they demonstrate your skills and your ability to solve real-world problems. 2) Choose a Platform Your GIS portfolio needs a home, and there are several platforms you can use to create it. Websites like GitHub, Behance, or even a personal website can serve as a platform for your portfolio. Esri’s ArcGIS StoryMaps, ArcGIS Experience Builder, or ArcGIS Hub are excellent tools that allows you to create interactive, visually compelling narratives that showcase your work. 3) Showcase a Variety of Skills When selecting projects for your portfolio, aim for diversity. Include projects that demonstrate your proficiency with different GIS tools and techniques, from spatial analysis and geocoding to data visualization and programming. This not only shows potential employers the breadth of your skills but also your adaptability in different areas of GIS. 4) Provide Context A map or a data visualization on its own might look impressive, but without context, it's just a pretty picture. For each project in your portfolio, provide a brief description that explains the problem you were trying to solve, the methods you used, and the results you achieved. This context is crucial for helping potential employers understand the impact of your work. 5) Keep It Updated Your portfolio should be a living document that evolves as your career progresses. Make it a habit to update your portfolio regularly with new projects and skills. This not only keeps your portfolio fresh but also serves as a reminder of your growth and accomplishments in the field. Leveraging Your Portfolio: How to Use It Effectively Once you've built your GIS portfolio, the next step is to leverage it in your job search and career development. Here are some strategies for making the most of your portfolio: 1) Use It in Job Applications When applying for GIS positions, include a link to your portfolio in your resume and cover letter. This allows potential employers to see firsthand what you can do, rather than just reading about it. 2) Bring It to Interviews In an interview, your portfolio can be a powerful tool for demonstrating your skills and experience. Consider bringing a tablet or laptop to the interview so you can walk the interviewer through your portfolio and discuss the projects in detail. 3) Share It on Professional Networks Platforms like LinkedIn are great for sharing your portfolio with a wider audience. Post updates about new projects you’ve added to your portfolio and include a link to your portfolio in your LinkedIn profile. This increases your visibility and can attract potential employers or collaborators. 4) Use It for Networking When networking at conferences or industry events, your portfolio can serve as a conversation starter. Whether you’re talking to potential employers or peers in the industry, being able to show them your work can leave a lasting impression. In the competitive and ever-evolving field of GIS, having a well-crafted portfolio is not just an option—it’s a necessity. A strong GIS portfolio serves as a powerful tool for showcasing your skills, telling your professional story, and navigating your career path. Whether you’re just starting out or looking to make a career transition, your portfolio can help you stand out, demonstrate your value, and open doors to new opportunities in the geospatial industry.

A new machine learning system can create height maps of urban environments from a single synthetic aperture radar (SAR) image, potentially accelerating disaster planning and response. Aerospace engineers at the University of the Bundeswehr in Munich claim their SAR2Height framework is the first to provide complete—if not perfect—three-dimensional city maps from a single SAR satellite. When an earthquake devastates a city, information can be in short supply. With basic services disrupted, it can difficult to assess how much damage occurred or where the need for humanitarian aid is greatest. Aerial surveys using laser ranging lidar systems provide the gold standard for 3D mapping, but such systems are expensive to buy and operate, even without the added logistical difficulties of a major disaster. Remote sensing is another option but optical satellite images are next to useless if the area is obscured by clouds or smoke. Synthetic aperture radar, on the other hand, works day or night, whatever the weather. SAR is an active sensor that uses the reflections of signals beamed from a satellite towards the Earth’s surface—the “synthetic aperture” part comes from the radar using the satellite’s own motion to mimic a larger antenna, to capture reflected signals with relatively long wavelengths. There are dozens of governmental and commercial SAR satellites orbiting the planet, and many can be tasked to image new locations in a matter of hours. However, SAR imagery is still inherently two-dimensional, and can be even trickier to interpret than photographs. This is partly due to an effect called radar layover where undamaged buildings appear to be toppling towards the sensor. “Height is a super complex topic in itself,” says Michael Schmitt, a professor at the University of the Bundeswehr. “There are a million definitions of what height is, and turning a satellite image into a meaningful height in a meaningful world geometry is a very complicated endeavor.” Schmitt and his colleague Michael Reclastarted by sourcing SAR images for 51 cities from the TerraSAR-X satellite, a partnership between the public German Aerospace Center and the private contractor Airbus Defence and Space. The researchers then obtained high quality height maps for the same cities, mostly generated by lidar surveys but some by planes or drones carrying stereo cameras. The next step was to make a one-to-one, pixel-to-pixel mapping between the height maps and the SAR images on which they could train a deep neural network. The results were amazing, says Schmitt. “We trained our model purely on TerraSAR-X imagery but out of the box, it works quite well on imagery from other commercial satellites.” He says the model, which takes only minutes to run, can predict the height of buildings in SAR images with an accuracy of around three meters—the height of a single story in a typical building. That means the system should be able to spot almost every building across a city that has suffered significant damage. Pietro Milillo, a professor of geosensing systems engineering at the University of Houston, hopes to use Schmitt and Recla’s model in an ongoing NASA-funded project on earthquake recovery. “We can go from a map of building heights to a map of probability of collapse of buildings,” he says. Later this month, Milillo intends to validate his application by visiting the site of an earthquake in Morocco last year that killed over 2,900 people. But the AI model is still far from perfect, warns Schmitt. It struggles to accurately predict the height of skyscrapers and is biased towards North American and European cities. This is because many cities in developing nations did not have regular lidar mapping flights to provide representational training data. The longer the gap between the lidar flight and the SAR images, the more buildings would have been built or replaced, and the less reliable the model’s predictions. Even in richer countries, “we’re really dependent on the slow revisit cycles of governments flying lidar missions and making the data publicly available,” says Carl Pucci, founder of EO59, a Virginia Beach, Va.-based company specializing in SAR software. “It just sucks. Being able to produce 3D from SAR alone would be really be a revolution.” Schmitt says the SAR2Height model now incorporates data from 177 cities and is getting better all time. “We are very close to reconstructing actual building models from single SAR images,” he says. “But you have to keep in mind that our method will never be as accurate as classic stereo or lidar. It will always remain a form of best guess instead of high-precision measurement.” source: ieee

Generative AI and 'text to GIS' are coming to ArcGIS Pro. GenAI is coming to replace most of the small-scale and basic analysis tasks, probably within 2-3 years. Here is a video of ArcGIS ecosystem using GenAI Assistant. https://mediaspace.esri.com/media/t/1_opret32t https://highearthorbit.com/articles/announcing-ai-assistants-for-arcgis/ And here is an updated roadmap for ArcGIS Pro - https://community.esri.com/t5/arcgis-pro-documents/arcgis-pro-roadmap-may-2024/ta-p/1419528/redirect_from_archived_page/true

1. Perkenalan Geodatabase (.gdb), Geodatabase adalah format penyimpanan data spasial yang digunakan dalam Sistem Informasi Geografis (SIG). Dikembangkan oleh Esri, geodatabase berfungsi sebagai wadah untuk menyimpan, mengelola, dan menganalisis data geografis secara efisien dalam bentuk yang terorganisir. Geodatabase memungkinkan pengguna untuk mengelola data spasial dan atributnya secara terintegrasi dalam satu basis data. 2. Geodatabase VS Shapefile. Geodatabase dan Shapefile adalah dua format data yang sering digunakan dalam Sistem Informasi Geografis (SIG) untuk menyimpan data spasial. Namun, keduanya memiliki perbedaan yang signifikan dalam hal kemampuan, efisiensi, dan fungsionalitas. Perbandingan antara keduanya meliputi struktur penyimpanan, kapasitas penyimpanan, dukungan data dan fungsi, skalabilitas dan kolaborasi, kinerja dan kompabilitas Pilih Geodatabase jika: - Anda bekerja dengan dataset besar dan kompleks. - Membutuhkan pengelolaan data terintegrasi (multi-layer, relasi, aturan topologi). - Menggunakan SIG pada skala organisasi besar. Pilih Shapefile jika: -Anda memerlukan format sederhana untuk berbagi data dengan banyak platform. - Dataset Anda kecil, dengan kebutuhan analisis yang sederhana. Meskipun shapefile masih banyak digunakan karena kesederhanaannya, geodatabase menawarkan kemampuan yang jauh lebih unggul untuk kebutuhan modern dalam SIG. 3. Ekspor SHP ke GDB GDB mampu membuat feature baru namun pada kesempatan ini kita akan mengekspor data SHP yang sudah ada ke GDB, selain menghemat waktu, kita juga dapat berlatih. selain SHP, format data yang populer lainnya adalah KML dan geoJSON. 4. Mengolah data survey lapangan dalam bentuk XLS, mengedit dan membersihkan data Sebelum di olah di ArcMAP, data lapangan dalam format XLS terlebih dahulu dibersihkan/cleaning seperti nama kolom yang tidak boleh ada spasi. 5. Ekspor XLS ke CSV Setelah dibersihkan, data XLS di ekspor ke CSV. 6. Plotting data sebaran titik survey CSV ke ArcMAP, data XY dalam Geographic Coordinate System (GCS) berformat decimal degree (DD) Data CSV kemudian ditambahkan dan plotting ke ArcMAP. Plotting atau menampilkan sebaran titik survey diatas kanvas ArcMAP dilakukan dengan menggunakan 2 (dua) kolom/field kombinasi X/Longitude/Bujur dan field Y/Latitude/Lintang sebagai titik koordinat bumi lokasi responden survey. Koordinat system yang digunakan dalam kursus kali ini adalah Geographic Coordinate System (GCS) dengan satuan derajat (Degree) dan berformat Decimal Degree/DD 7. Ekspor data plotting ke GDB Sebaran titik survey yang telah di tambahkan di kanvas ArcMAP tersimpan sementara di memori (temporary layer), untuk membuatnya permanen maka kita akan ekspor data sebaran titik ini ke GDB 8. Membuat model spasial kita akan membuat model spasial dari sebaran titik survey yang telah tersimpan di GDB. Model spasial ini dapat berbentuk thematik dan khoropleth. Model spasial akan memberikan gambaran lebih jelas bagaimana data ini tersebar berdasarkan data atribut yang diperoleh seperti model spasial usia, model spasial omset perbulan, model spasial omset pertahun dan lainnya. 9. Mendesain Layout dalam ArcMAP document (MXD). Kita akan membuat layout di ArcMAP. Kita membuat layout untuk masing-masing model spasial di atas. download: https://rapidgator.net/file/2f100a5bfa0ca590da1b0572a8d22163/SANET.STProcessingSurveyDatainGCSWithArcGISDesktop10.8.part1.rar.html https://rapidgator.net/file/c81950916039d65e0ecfd400014cbaa3/SANET.STProcessingSurveyDatainGCSWithArcGISDesktop10.8.part2.rar.html

The fall update to Global Mapper includes numerous usability updates, processing improvements, and with Pro, beta access to the Global Mapper Insight and Learning Engine which contains deep learning-based image analysis tools. Global Mapper is a complete geospatial software solution. The Standard version excels at basic vector, raster, and terrain editing, with Global Mapper Pro expanding the toolset to support drone-collected image processing, point cloud classification and extraction, and many more advanced image and terrain analysis options. Version 26.0 of Global Mapper Standard focuses on ease-of-use updates to improve the experience and efficiency of the software. A Global Search acts as a toolbox to locate any tool within the program, and a source search in the online data streaming tool makes it easier to bring online data into the application. Updates for working with 3D data include construction site planning to keep all edited terrain for a flattened site within a selected area and the ability to finely adjust the vertex position of 3D lines in reference to terrain in the Path Profile tool. Perhaps the largest addition to Global Mapper Pro v26.0 is the availability of the new Insight and Learning Engine which provides deep learning-based image analysis. Available with Global Mapper Pro for a limited time for users to test and explore, users can leverage built-in models for building extraction, vehicle detection, or land cover classification. These models can even be fine-tuned with iterative training to optimize the analysis for the data area.

Responding to the escalating threats from climate change, biodiversity loss, pollution and extreme weather and the need to take action to address these threats, this forward-looking strategy outlines a bold vision for Earth science through to 2040. By leveraging advanced satellite-based monitoring of our planet, ESA aims to provide critical data and knowledge to guide action and policy for a more sustainable future. ESA’s Director of Earth Observation Programmes, Simonetta Cheli, said, “As a space agency, it is our duty to harness the unique power of Earth observing technology to inform the critical decisions that will shape our future. “Our new Earth Observation Science Strategy underscores a science-first approach where satellite technology provides data that contribute to our collective understanding of the Earth system as a whole, so that solutions can be found to address global environmental challenges.” “The choices we make today help create a more sustainable world and propel the transformation towards a resilient, thriving global society.” The new Science Strategy presents a bold and ambitious vision for the future of ESA’s Earth Observation Programmes. It shifts focus towards understanding the feedbacks and interconnections within the Earth system, rather than targeting specific Earth system domains.

You're a hotshot working to contain a wildfire. The conflagration jumps the fire line, forcing your crew to flee using pre-determined escape routes. At the start of the day, the crew boss estimated how long it should take to get to the safety zone. With the flames at your back, you check your watch and hope they were right. Firefighters mostly rely on life-long experience and ground-level information to choose evacuation routes, with little support from digital mapping or aerial data. The tools that do exist tend to consider only a landscape's steepness when estimating the time it takes to traverse across terrain. However, running up a steep road may be quicker than navigating a flat boulder field or bushwacking through chest-high shrubs. Firefighters, disaster responders, rural health care workers and professionals in myriad other fields need a tool that incorporates all aspects of a landscape's structure to estimate travel times. In a new study, researchers from the University of Utah introduced Simulating Travel Rates in Diverse Environments (STRIDE), the first model that incorporates ground roughness and vegetation density, in addition to slope steepness, to predict walking travel times with unprecedented accuracy. "One of the fundamental questions in firefighter safety is mobility. If I'm in the middle of the woods and need to get out of here, what is the best way to go and how long will it take me?" said Mickey Campbell, research assistant professor in the School of Environment, Society and Sustainability (ESS) at the U and lead author of the study. The authors analyzed airborne Light Detection and Ranging (LiDAR) data and conducted field trials to develop a remarkably simple, accurate equation that identifies the most efficient routes between any two locations in wide-ranging settings, from paved, urban environments to off-trail, forested landscapes. They found that STRIDE consistently chose routes resembling paths that a person would logically seek out—a preference for roads and trails and paths of least resistance. STRIDE also produced much more accurate travel times than the standard slope-only models that severely underestimated travel time. "If the fire reaches a firefighter before they reach safety, the results can be deadly, as has happened in tragedies such as the 2013 Yarnell Hill fire," said Campbell. "STRIDE has the potential to not only improve firefighter evacuation but also better our understanding of pedestrian mobility across disciplines from defense to archaeology, disaster response and outdoor recreation planning." Airborne estimates of on-the-ground travel STRIDE is the first comprehensive model to use airborne LiDAR data to map two underappreciated factors that inhibit off-road travel—vegetation density and ground surface roughness—as well as steepness. LiDAR is commonly used to map the structure of a landscape from the air, Campbell explained. A LiDAR-equipped plane has sensors that shoot millions of laser pulses in all directions, which bounce back and paint a detailed map of structures on the ground. The laser pulses bounce off leaf litter, gravel, boulders, shrubs and tree canopies to build three-dimensional maps of terrain and vegetation with centimeter-level precision. The authors compared STRIDE performance against travel rates gleaned from three field experiments, in which volunteers walked along 100-meter-transects through areas with existing LiDAR data. "Getting travel times from a variety of volunteers allowed us to account for a range of human performance so we can make the most accurate predictions of travel rates in a diversity of environments," said co-author Philip Dennison, professor and director of ESS. The first field trials were in September of 2016. At the time, LiDAR datasets were relatively rare in the western U.S. Over the last decade, the U.S. Geological Society has developed LiDAR maps covering most of the country. "When we first started looking into wildland firefighter-mobility a decade ago, there were lots of people studying how fire spreads across the landscape, but very few people were working on the problem of how firefighters move across the landscape," said Campbell, then a doctoral student in Dennison's lab at ESS. "Only by combining these two pieces of information can we truly understand how to improve firefighter safety." That study, published in 2017, was the first attempt to map escape routes for wildland firefighters using LiDAR. The second trial took place in August of 2023 in the central Wasatch Mountains of Utah to capture a wider set of undeveloped, off-path landscape conditions than did the first experiment, including nearly impassibly steep slopes and extremely dense vegetation. The final experiment was in January of 2024 in Salt Lake City to test the STRIDE model in an urban environment. In total, about 50 volunteers walked more than 40 100-meter transects of highly varied terrain. Putting it together The study compared STRIDE against a slope-only model to generate the most efficient routes, or the least-cost paths, in the mountains surrounding Alta Ski Resort in the Wasatch Mountains, Utah. Geographers and archaeologists have been using least-cost path modeling to simulate human movement for decades; however, to date most have relied almost exclusively on slope as the sole landscape impediment. The authors imagined a scenario in which emergency responders are planning to rescue an injured hiker. From a central point, they chose 1,000 random locations for the hiker and asked both models to find the least-cost path. STRIDE chose established roads around the ski areas, followed trails and in some cases major ski slopes, to avoid patches of forest or dense vegetation. STRIDE reused established paths as long as possible before branching off, reinforcing the idea that STRIDE identified the routes most intuitive for somebody on the ground. "The really cool thing is that we didn't supply the algorithm with any knowledge of existing transportation networks. It just knew to take the roads because they're smoother, not vegetated and tend to be less steep," said Campbell. In contrast, the slope-only model had few overlapping pathways, with little regard for roads or trails. It sent rescuers through dense vegetation, dangerous scree fields and forested areas. The authors believe that STRIDE will have an immediate impact in the real world—they've made the STRIDE model publicly available so that anyone with LiDAR data and gumption can make their work or recreation more efficient, with a higher safety margin. "If you don't consider the vegetation cover and ground-surface material, you're going to significantly underestimate your total travel time. The U.S. Forest Service has been really supportive of this travel rate research because they recognize the inherent value of understanding firefighter mobility," said Campbell. "That's what I love about this work. It's not just an academic exercise, but it's something that has real, tangible implications for firefighters and for professionals in so many other fields." The authors recently used a slope-based travel rate model to update the U.S. Forest Service Ground Evacuation Time (GET) layer, which allows wildland firefighters to estimate travel time to the nearest medical facility from any location in the contiguous U.S. Campbell hopes to use STRIDE to improve GET, allowing for more accurate estimates of evacuation times. links: https://www.nature.com/articles/s41598-024-71359-6

Dear TerrSet/IDRISI Community, We are writing to share important and exciting news: Effective December 2, 2024, a new open access version of TerrSet/IDRISI will be released. Made possible by Clark Lab's merger with the new Clark Center for Geospatial Analytics (Clark CGA), the new version will be free to all users. This is the realization of a 37-year dream – to make the software accessible to everyone, everywhere. Between now and December 2, a series of progressive discounts will be applied as follows: Starting Aug. 28, 2024, all licenses and renewals will be 25% off. Starting Oct. 1, 2024, all licenses and renewals will be 50% off. Low-income country discounts, organizational pricing, and student starter licenses will end. Starting Nov. 1, 2024, all licenses and renewals will be 75% off. On Dec. 2, 2024, liberaGIS will be released and all its licenses will be free.

Everything from drones to airplanes, ships, and cars are equipped with GPS units to help them navigate around the world. This information is crucial not only for powering autonomous navigation systems, but also for supplying human operators with the information they need to get where they are going. But while this technology has become essential in the modern world, our reliance on it is somewhat concerning. In some locations GPS signals are blocked by obstructions, so the systems that rely on them are useless. Worse yet, GPS signals can be intentionally spoofed or jammed, which could lead to widespread chaos and tragedy. These problems could be averted by using self-contained motion sensors rather than signals from a constellation of satellites. But that would require motion sensors thousands of times more accurate than the types that we have in our smartphones and other consumer electronics. The technology does exist today, but in order to be accurate enough to replace GPS, a quantum inertial measurement unit is the only option available. These systems require six atom interferometers, with each being large enough to fill a small room. That is not exactly practical for the vast majority of applications, and as you might expect, these systems are also extremely expensive. Researchers at Sandia National Laboratories have been working on a much more compact atom interferometer, however, which could make precise, GPS-free navigation a practical reality in the near future. The new system is based on Photonic Integrated Circuits (PICs), which make it significantly more compact than the traditional laser systems used in atom interferometers. The new technology is also more resistant to vibrations and shocks, making it ideal for use in challenging environments. PICs are small, durable chips that can perform the same functions as larger, more complex laser systems. These chips integrate various components — like modulators and amplifiers — onto a single platform, making the entire system smaller, more robust, and easier to produce. One key innovation is the development of a silicon photonic modulator, which is crucial for controlling the light in these systems. This modulator allows the system to generate and manage multiple laser frequencies from a single source, eliminating the need for multiple lasers. These novel modulators were also noted to substantially reduce unwanted echoes called sidebands that plague existing technologies. The result is a compact, high-performance laser system that can be used in a variety of advanced applications, including quantum sensors like atomic clocks and gyroscopes. Overall, this represents a significant step forward in making these advanced sensing technologies more practical and deployable in real-world situations. The team also pointed out that the applications of their technology extend well beyond navigation. These sensors could, for example, be used to locate natural resources hidden beneath the ground by observing how they alter Earth’s gravitational force. Further potential applications exist in enhancing LIDAR sensors, quantum computing, and optical communications.

has been discuss in their forum https://www.agisoft.com/forum/index.php?topic=2420.0

Hi, adnan0001 If you go for icons, another resource can be found here https://mapicons.mapsmarker.com/ You can change the color of icons online and export those icons to use in arcmap or other softwares.