A new deep-learning algorithm studies aerial photographs after fires to identify damage. How it works: From satellite images taken before and after the California wildfires of 2017, researchers created a data set of buildings that were either damaged or left unscathed. The results: They tweaked a pre-trained ImageNet neural network and got it to spot damaged buildings with an accuracy of up to 85 percent. Why it matters: After a disaster, pinpointing the hardest-hit areas could save lives and help with relief efforts. The researchers also released the data set to the public, which could improve other research that requires satellite images, like conservation and developmental aid work.

We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of scale. We design easy-to-train, fully-convolutional neural networks able to learn scale-specific features. Once chained appropriately, they perform global registration in linear time, getting rid of gradient descent schemes by predicting directly the deformation.We show their performance in terms of quality and speed through various tasks of remote sensing multimodal image alignment. In particular, we are able to register correctly cadastral maps of buildings as well as road polylines onto RGB images, and outperform current keypoint matching methods.

When you have large data sets of satellite or drone imagery that you have to process to create predictions, classification, or clustering – machine learning (ML) is the way to go. Indeed, ML has started to play a critical role in spatial problem solving given its potential to rapidly scan and unlock insights from petabytes of pixels obtained from hundreds of satellites and drones that are constantly orbiting earth. Orbital Insight, for example, applies machine learning and computer vision technologies to interpret data at petabyte scale to make it actionable for better business and policy decisions. The California based company has developed a powerful method that blends satellite imagery, deep learning, and data science for monitoring fresh-water supplies at local and global scale. Good news is that you don't have to be Orbital or in California,USA to also deploy machine learning. The proliferation of opensource platforms has made machine learning a lot easier to implement both on single personal computers and at scale, and in most popular programming or scripting languages.

Estimating economic and developmental parameters such as poverty levels of a region from satellite imagery is a challenging problem that has many applications. We propose a two step approach to predict poverty in a rural region from satellite imagery. First, we engineer a multi-task fully convolutional deep network for simultaneously predicting the material of roof, source of lighting and source of drinking water from satellite images. Second, we use the predicted developmental statistics to estimate poverty. Using full-size satellite imagery as input, and without pre-trained weights, our models are able to learn meaningful features including roads, water bodies and farm lands, and achieve a performance that is close to the optimum. In addition to speeding up the training process, the multi-task fully convolutional model is able to discern task specific and independent feature representations.

Label Maker is a python library to help in extracting insight from satellite imagery. Label Maker creates machine-learning-ready training data for most popular ML frameworks, including Keras, Tensor Flow, and MXNet. It pulls data from OpenStreetMap and combines that with imagery sources like Mapbox or Digital Globe to create a single file for use in training machine learning algorithms. Supervised learning methods require two things: satellite imagery and ground-truth labels. If you're looking to train a model in Potsdam or a few other select cities, there are good datasets already available.

Growers in Illinois are looking for new ways to expand their use of technology, and artificial intelligence is emerging as their way of embracing the future of farm production. Chad Colby, an agricultural technologist and creator of Colby AgTech, said Illinois farmers are starting to look into robotics as a way of managing their crops and are using technology they wouldn't have considered a few years ago. Remote sensing in soil and from the sky has become popular, according to Colby. "Right now, today, the use is coming from satellites, drones and aircraft; but over the next couple years, you'll see those benefits expand as guys start to utilize the benefits of new technology in our soils," Colby said. The costs for using artificial intelligence has gotten cheaper, Colby said.

NEW DELHI: New technologies such as Artificial Intelligence (AI), Cloud Machine Learning, Satellite Imagery and advanced analytics are empowering small-holder farmers in India to increase their income through higher crop yield and greater price control, Microsoft India said. In a few dozen villages in Telengana, Maharashtra and Madhya Pradesh, farmers are receiving automated voice calls that tell them whether their cotton crops are at risk of a pest attack, based on weather conditions and crop stage. In Karnataka, the government can get price forecasts for essential commodities such as tur (split red gram) three months in advance for planning the Minimum Support Price (MSP). "Sowing date as such is very critical to ensure that farmers harvest a good crop. And if it fails, it results in loss as a lot of costs are incurred for seeds, as well as the fertilizer applications," Suhas P. Wani, Director, Asia Region, of the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT), said in a Microsoft blog post.

New technologies such as Artificial Intelligence (AI), Cloud Machine Learning, Satellite Imagery and advanced analytics are empowering small-holder farmers in India to increase their income through higher crop yield and greater price control, Microsoft India said. In a few dozen villages in Telengana, Maharashtra and Madhya Pradesh, farmers are receiving automated voice calls that tell them whether their cotton crops are at risk of a pest attack, based on weather conditions and crop stage. In Karnataka, the government can get price forecasts for essential commodities such as tur (split red gram) three months in advance for planning the Minimum Support Price (MSP). "Sowing date as such is very critical to ensure that farmers harvest a good crop. And if it fails, it results in loss as a lot of costs are incurred for seeds, as well as the fertilizer applications," Suhas P. Wani, Director, Asia Region, of the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT), said in a Microsoft blog post.

Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight. In this paper, we propose a novel method of inducing spatial aggregations as a component of the machine learning process, yielding regional model features whose construction is driven by model prediction performance rather than prior assumptions. Our results demonstrate that Genetic Programming is particularly well suited to this type of feature construction because it can automatically synthesize appropriate aggregations, as well as better incorporate them into predictive models compared to other regression methods we tested. In our experiments we consider a specific problem instance and real-world dataset relevant to predicting snow properties in high-mountain Asia.

Deep learning (DL), a new-generation artificial neural network research, has made profound strides in recent years. This review paper is intended to provide water resources scientists with a simple technical overview, trans-disciplinary progress update, and potentially inspirations about DL. Effective architectures, more accessible data, advances in regularization, and new computing power enabled the success of DL. A trans-disciplinary review reveals that DL is rapidly transforming myriad scientific disciplines including high-energy physics, astronomy, chemistry, genomics and remote sensing, where systematic DL toolkits, innovative customizations, and sub-disciplines have emerged. However, with a few exceptions, its adoption in hydrology has so far been gradual. The literature suggests that novel regularization techniques can effectively prevent high-capacity deep networks from overfitting. As a result, in most scientific disciplines, DL models demonstrated superior predictive and generalization performance to conventional methods. Meanwhile, less noticed is that DL may also serve as a scientific exploratory tool. A new area termed "AI neuroscience", has been born. This budding sub-discipline is accumulating a significant body of work, e.g., distilling knowledge obtained in DL networks to interpretable models, attributing decisions to inputs via back-propagation of relevance, or visualization of activations. These methods are designed to interpret the decision process of deep networks and derive insights. While scientists so far have mostly been using customized, ad-hoc methods for interpretation, vast opportunities await for DL to propel advancement in water science.