Understanding GIS Mapping GIS Mapping is a technology and process used to capture, store, analyze, manage, and visualize geographic or spatial data. It combines geographical information such as locations and terrain features, with various types of data like environmental, social, economic, and demographic information, to create detailed and layered maps. These maps are powerful tools for understanding and interpreting spatial relationships, patterns, and trends.   Components of GIS Mapping Key components of GIS mapping include:   1. Hardware. The hardware is the tangible aspect of GIS mapping technology. This includes computers, GPS devices, drones, and other equipment used to collect, process, and analyze geographic data.   2. Software. GIS mapping provides a platform for creating maps, conducting spatial analyses, and sharing geographic information.   3. Data. Spatial data is the core of GIS mapping. It encompasses information about specific locations, attributes, and relationships. This data can come from various sources, such as satellite imagery, surveys, government databases, or user-generated content.   4. People. Skilled individuals, such as GIS analysts, cartographers, geographers, and geospatial scientists, are essential for using GIS technology effectively. They design, develop, and apply GIS solutions to address specific problems or research questions.   GIS mapping allows users to perform a wide range of spatial analyses like measuring distances, determining optimal routes, assessing environmental changes, and identifying patterns within data. Therefore, it has a significant impact on humanitarian assistance and disaster preparedness and response. Now, what does this transformative impact look like?   How GIS Mapping Transforms Humanitarian Assistance It Enhances Disaster Response When disasters strike (and they usually do), whether they take the form of a natural catastrophe or a man-made crisis, every second counts. Key decision-makers therefore need adequate data and spatial information to respond proactively. This is where GIS mapping technology shines. Real-time data on the location and extent of a disaster, along with intricate details about affected areas and population distribution, enable aid agencies to make well-informed decisions, coordinate efforts, and manage resources effectively. Crucially, the ability to visualize and analyze information on a map empowers responders to prioritize their actions based on the most pressing needs. This ultimately saves lives.   GIS Technology Helps Map Vulnerable Populations In humanitarian work, the overarching goal is to help those who are most in need. Humanitarian assistance, therefore, relies heavily on the ability to identify and map ‘vulnerable’ populations. This is where GIS technologies play a crucial role. GIS mapping provides a powerful tool for identifying vulnerable populations, whether they are refugees fleeing conflict, communities at risk from disease outbreaks, or marginalized groups living in impoverished regions.   Therefore, by overlaying geographic data with information on poverty rates, access to healthcare and food security, aid workers can make informed decisions about where and how to allocate resources effectively. This targeted approach ensures that aid reaches the individuals and communities that require it the most.   GIS Mapping Provides Real-time Data One of the most remarkable features of GIS mapping in humanitarian aid is its ability to provide real-time data. This is usually in the form of satellite imagery. This capability is particularly crucial in disaster management, where timely and accurate information is of paramount importance. For example, during a hurricane, GIS technology can track the storm’s path, predict areas likely to be impacted and facilitate evacuation planning. It can also assess damage immediately after the event, thereby allowing for a rapid and well-coordinated response. This ‘bird’s eye view’ of disaster-affected areas equips humanitarian workers with the data needed to make informed decisions and deploy resources efficiently. Additionally, with real-time data, there’s flexibility in managing situations on the go.   GIS Mapping Helps Track and Monitor Epidemics and Disease Outbreaks GIS mapping plays a pivotal role in monitoring and controlling disease outbreaks. During epidemics such as the Ebola crisis in West Africa, GIS technology tracked the spread of the disease, identified hotspots of infection and helped health workers isolate cases and trace contacts. These insights were crucial in containment efforts and ultimately contributed to the control of the epidemic. By visualizing the geographic spread of the disease, humanitarian organizations could direct resources to the areas that needed them most, effectively limiting the outbreak’s reach.   Enroll in: GIS in Monitoring and Evaluation Course   It Enhances Disaster Risk Reduction and Management In the field of disaster management, preparedness is often the best form of defense. GIS mapping aids in identifying disaster-prone regions, allowing communities to plan for potential crises. By creating detailed hazard maps, which include flood risk assessments, earthquake-prone areas, and other environmental hazards, this technology helps in developing preparedness plans and mitigating the impact of disasters. The ability to visualize potential risks empowers communities to take proactive measures, such as reinforcing infrastructure, developing evacuation plans, and building resilient shelters.   Enroll in: GIS For WASH Programmes Course   Crowdsourced Mapping Crowdsourced mapping has proven to be a remarkable revelation to humanitarian aid. It’s a collaborative approach to creating and updating maps and geographic information using contributions from the general public. This method relies on the collective efforts of volunteers who provide geographic data, typically using digital tools.  Initiatives like OpenStreetMap have harnessed these efforts to contribute data on roads, buildings, and infrastructure in disaster-affected areas. This grassroots approach has been instrumental in improving the accuracy and completeness of maps in areas that were previously unmapped. Crucially, humanitarian organizations can then use this data for response efforts, making it a remarkable example of how technology and global collaboration can save lives. Therefore, this collective action not only aids in immediate response but also contributes to the resilience of affected communities. Click HERE to read more.      

Multimodal machine learning models have been surging in popularity, marking a significant evolution in artificial intelligence (AI) research and development. These models, capable of processing and integrating data from multiple modalities such as text, images, and audio, are of great importance due to their ability to tackle complex real-world problems that traditional unimodal models struggle with. The fusion of diverse data types enables these models to extract richer insights, enhance decision-making processes, and ultimately drive innovation. Among the burgeoning applications of multimodal machine learning, Visual Question Answering (VQA) models have emerged as particularly noteworthy. VQA models possess the capability to comprehend both images and accompanying textual queries, providing answers or relevant information based on the content of the visual input. This capability opens up avenues for interactive systems, enabling users to engage with AI in a more intuitive and natural manner.   However, despite their immense potential, the deployment of VQA models, especially in critical scenarios such as disaster recovery efforts, presents unique challenges. In situations where internet connectivity is unreliable or unavailable, deploying these models on tiny hardware platforms becomes essential. Yet the deep neural networks that power VQA models demand substantial computational resources, rendering traditional edge computing hardware solutions impractical. Inspired by optimizations that have enabled powerful unimodal models to run on tinyML hardware, a team led by researchers at the University of Maryland has developed a novel multimodal model called TinyVQA that allows extremely resource-limited hardware to run VQA models. Using some clever techniques, the researchers were able to compress the model to the point that it could run inferences in a few tens of milliseconds on a common low-power processor found onboard a drone. In spite of this substantial compression, the model was able to maintain acceptable levels of accuracy. To achieve this goal, the team first created a deep learning VQA model that is similar to other state of the art algorithms that have been previously described. This model was far too large to use for tinyML applications, but it contained a wealth of knowledge. Accordingly, the model was used as a teacher for a smaller student model. This practice, called knowledge distillation, captures much of the important associations found in the teacher model, and encodes them in a more compact form in the student model. In addition to having fewer layers and fewer parameters, the student model also made use of 8-bit quantization. This reduces both the memory footprint and the amount of computational resources that are required when running inferences. Another optimization involved swapping regular convolution layers out in favor of depthwise separable convolution layers — this further reduced model size while having a minimal impact on accuracy. Having designed and trained TinyVQA, the researchers evaluated it by using the FloodNet-VQA dataset. This dataset contains thousands of images of flooded areas captured by a drone after a major storm. Questions were asked about the images to determine how well the model understood the scenes. The teacher model, which weighs in at 479 megabytes, was found to have an accuracy of 81 percent. The much smaller TinyVQA model, only 339 kilobytes in size, achieved a very impressive 79.5 percent accuracy. Despite being over 1,000 times smaller, TinyVQA only lost 1.5 percent accuracy on average — not a bad trade-off at all! In a practical trial of the system, the model was deployed on the GAP8 microprocessor onboard a Crazyflie 2.0 drone. With inference times averaging 56 milliseconds on this platform, it was demonstrated that TinyVQA could realistically be used to assist first responders in emergency situations. And of course, many other opportunities to build autonomous, intelligent systems could also be enabled by this technology. source: hackster.io

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

Satellite images analyzed by AI are emerging as a new tool in finding unmapped roads that bring environmental destruction to wilderness areas. James Cook University's Distinguished Professor Bill Laurance was co-author of a study analyzing the reliability of an automated approach to large-scale road mapping, using convolutional neural networks trained on road data, using satellite images. He said the Earth is experiencing an unprecedented wave of road building, with some 25 million kilometers of new paved roads expected by mid-century. "Roughly 90% of all road construction is occurring in developing nations including many tropical and subtropical regions of exceptional biodiversity. "By sharply increasing access to formerly remote natural areas, poorly regulated road development triggers dramatic increases in environmental disruption due to activities such as logging, mining and land clearing," said Professor Laurance. He said many roads in such regions, both legal and illegal, are unmapped, with road-mapping studies in the Brazilian Amazon, Asia-Pacific and elsewhere regularly finding up to 13 times more road length than reported in government or road databases. "Traditionally, road mapping meant tracing road features by hand, using satellite imagery. This is incredibly slow, making it almost impossible to stay on top of the global road tsunami," said Professor Laurance. The researchers trained three machine-learning models to automatically map road features from high-resolution satellite imagery covering rural, generally remote and often forested areas of Papua New Guinea, Indonesia and Malaysia. "This study shows the remarkable potential of AI for large-scale tasks like global road-mapping. We're not there yet, but we're making good progress," said Professor Laurance. "Proliferating roads are probably the most important direct threat to tropical forests globally. In a few more years, AI might give us the means to map and monitor roads across the world's most environmentally critical areas." journal: https://www.mdpi.com/2072-4292/16/5/839