The platform uses geospatial data and satellite imagery to provide data-based applications for mineral exploration and discovery and promises to increase hypothesis testing and the speed of the exploration lifecycle. "Traditionally, remote sensing is carried out by specialists (remote sensing geologists) on behalf of the mineral exploration team. Although they still have a role in supporting the process, the Descartes Labs platform puts the technology into the hands of the exploration geologists who know the project areas the best. By leveraging the data obtained from satellite and airborne imagery, they can accelerate their hypothesis formulation and exploration strategies to find new deposits," James Orsulak, senior director of business and sales at Descartes Labs, told MINING.COM. MDC: Your platform puts emphasis on the data refinery.

Today was a fun and exciting day at the Esri Federal GIS Conference 2021 highlighted by great user presentations, inspiring talks, and a powerful technology showcase. The imagery and remote sensing demonstration showed how AI was effectively put to use in a SAAS environment. Driving the AI was a pre-trained model that is downloadable for all users from ArcGIS Living Atlas. This is just one of the many models that have been released on ArcGIS Living Atlas of the World. Ever since the pre-trained geospatial deep learning models were released on ArcGIS Living Atlas, they have been well received.

Recent years have witnessed rapid growth in satellite-based approaches to quantifying aspects of land use, especially those monitoring the outcomes of sustainable development programs. Burke et al. reviewed this recent progress with a particular focus on machine-learning approaches and artificial intelligence methods. Drawing on examples mostly from Africa, they conclude that satellite-based methods enhance rather than replace ground-based data collection, and progress depends on a combined approach. Science , this issue p. [eabe8628][1] ### BACKGROUND Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. For instance, good measures are needed to monitor progress toward sustainability goals and evaluate interventions designed to improve development outcomes. Traditional approaches to measurement of many key outcomes rely on household surveys that are conducted infrequently in many parts of the world and are often of low accuracy. The paucity of ground data stands in contrast to the rapidly growing abundance and quality of satellite imagery. Multiple public and private sensors launched in recent years provide temporal, spatial, and spectral information on changes happening on Earth’s surface. Here we review a rapidly growing scientific literature that seeks to use this satellite imagery to measure and understand various outcomes related to sustainable development. We pay particular attention to recent approaches that use methods from artificial intelligence to extract information from images, as these methods typically outperform earlier approaches and enable new insights. Our focus is on settings and applications where humans themselves, or what they produce, are the outcome of interest and on where these outcomes are being measured using satellite imagery. ### ADVANCES We describe and synthesize the variety of approaches that have been used to extract information from satellite imagery, with particular attention given to recent machine learning–based approaches and settings in which training data are limited or noisy. We then quantitatively assess predictive performance of these approaches in the domains of smallholder agriculture, economic livelihoods, population, and informal settlements. We show that satellite-based performance in predicting these outcomes is reasonably strong and improving. Performance improvements have come through a combination of more numerous and accurate training data, more abundant and higher-quality imagery, and creative application of advances in computer vision to satellite inputs and sustainability outcomes. Further, our analyses suggest that reported model performance likely understates true performance in many settings, given the noisy data on which predictions are evaluated and the types of noise typically observed in sustainability applications. For multiple outcomes of interest, satellite-based estimates can now equal or exceed the accuracy of traditional approaches to outcome measurement. We describe multiple methods through which the true performance of satellite-based approaches can be better understood. Integration of satellite-based sustainability measurements into research has been broad, and we describe applications in agriculture, fisheries, health, and economics. Documented uses of these measurements in public-sector decision-making are rarer, which we attribute in part to the novelty of the approaches, their lack of interpretability, and the potential benefits to some policy-makers of not having certain outcomes be measured. ### OUTLOOK The largest constraint to satellite-based model performance is now training data rather than imagery. While imagery has become abundant, the scarcity and frequent unreliability of ground data make both training and validation of satellite-based models difficult. Expanding the quantity and quality of such data will quickly accelerate progress in this field. Other opportunities for advancement include improvements in model interpretability, fusion of satellites with other nontraditional data that provide complementary information, and more-rigorous evaluation of satellite-based approaches (relative to available alternatives) in the context of specific use cases. Nevertheless, despite the current and future promise of satellite-based approaches, we argue that these approaches will amplify rather than replace existing ground-based data collection efforts in most settings. Many outcomes of interest will likely never be accurately estimated with satellites; for outcomes where satellites do have predictive power, high-quality local training data can nearly always improve model performance. ![Figure][2] Increasing collection of satellite imagery can help measure livelihood outcomes in areas where ground data are sparse. (Left) Interval between nationally representative economic surveys over the past three decades shows long lags in many developing countries. (Middle) Recently added public and private satellites have broken the traditional trade-off between temporal and spatial resolution. (Right) Performance in measuring the presence of informal settlements, crop yields on smallholder agricultural plots, and village-level asset wealth. R 2, coefficient of determination. Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses satellite imagery to understand these outcomes, with a focus on approaches that combine imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and improving resolution (spatial, temporal, and spectral) of satellite imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of model performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight research directions for the field. [1]: /lookup/doi/10.1126/science.abe8628 [2]: pending:yes

Road transportation is one of the largest sectors of greenhouse gas (GHG) emissions affecting climate change. Tackling climate change as a global community will require new capabilities to measure and inventory road transport emissions. However, the large scale and distributed nature of vehicle emissions make this sector especially challenging for existing inventory methods. In this work, we develop machine learning models that use satellite imagery to perform indirect top-down estimation of road transport emissions. Our initial experiments focus on the United States, where a bottom-up inventory was available for training our models. We achieved a mean absolute error (MAE) of 39.5 kg CO$_{2}$ of annual road transport emissions, calculated on a pixel-by-pixel (100 m$^{2}$) basis in Sentinel-2 imagery. We also discuss key model assumptions and challenges that need to be addressed to develop models capable of generalizing to global geography. We believe this work is the first published approach for automated indirect top-down estimation of road transport sector emissions using visual imagery and represents a critical step towards scalable, global, near-real-time road transportation emissions inventories that are measured both independently and objectively.

Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to process and analyze these hyperspectral (HS) products mainly by means of seasoned experts. However, with the ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges on reducing the burden of manual labor and improving efficiency. For this reason, it is, therefore, urgent to develop more intelligent and automatic approaches for various HS RS applications. Machine learning (ML) tools with convex optimization have successfully undertaken the tasks of numerous artificial intelligence (AI)-related applications. However, their ability in handling complex practical problems remains limited, particularly for HS data, due to the effects of various spectral variabilities in the process of HS imaging and the complexity and redundancy of higher dimensional HS signals. Compared to the convex models, non-convex modeling, which is capable of characterizing more complex real scenes and providing the model interpretability technically and theoretically, has been proven to be a feasible solution to reduce the gap between challenging HS vision tasks and currently advanced intelligent data processing models.

During the last decade, several research works have focused on providing novel deep learning methods in many application fields. However, few of them have investigated the weight initialization process for deep learning, although its importance is revealed in improving deep learning performance. This can be justified by the technical difficulties in proposing new techniques for this promising research field. In this paper, a survey related to weight initialization techniques for deep algorithms in remote sensing is conducted. This survey will help practitioners to drive further research in this promising field. To the best of our knowledge, this paper constitutes the first survey focusing on weight initialization for deep learning models.

Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms' applications have been conducted in an attempt to summarize the amount of information produced in its subfields. Recently, Unmanned Aerial Vehicles (UAV) based applications have dominated aerial sensing research. However, a literature revision that combines both "deep learning" and "UAV remote sensing" thematics has not yet been conducted. The motivation for our work was to present a comprehensive review of the fundamentals of Deep Learning (DL) applied in UAV-based imagery. We focused mainly on describing classification and regression techniques used in recent applications with UAV-acquired data. For that, a total of 232 papers published in international scientific journal databases was examined. We gathered the published material and evaluated their characteristics regarding application, sensor, and technique used. We relate how DL presents promising results and has the potential for processing tasks associated with UAV-based image data. Lastly, we project future perspectives, commentating on prominent DL paths to be explored in the UAV remote sensing field. Our revision consists of a friendly-approach to introduce, commentate, and summarize the state-of-the-art in UAV-based image applications with DNNs algorithms in diverse subfields of remote sensing, grouping it in the environmental, urban, and agricultural contexts.

Building on recent theoretical proposals, classification techniques, so focusing on remote sensing applications, initial practical studies suggest that these concepts have the and discuss the bottlenecks of performing these algorithms possibility to be implemented in the laboratory, under strictly on currently available open source platforms. Initial controlled conditions [4], and open the way to the evolution results demonstrate feasibility. Next steps include expanding of their employment and validation.

Remote sensing (RS) scene classification is a challenging task to predict scene categories of RS images. RS images have two main characters: large intra-class variance caused by large resolution variance and confusing information from large geographic covering area. To ease the negative influence from the above two characters. We propose a Multi-granularity Multi-Level Feature Ensemble Network (MGML-FENet) to efficiently tackle RS scene classification task in this paper. Specifically, we propose Multi-granularity Multi-Level Feature Fusion Branch (MGML-FFB) to extract multi-granularity features in different levels of network by channel-separate feature generator (CS-FG). To avoid the interference from confusing information, we propose Multi-granularity Multi-Level Feature Ensemble Module (MGML-FEM) which can provide diverse predictions by full-channel feature generator (FC-FG). Compared to previous methods, our proposed networks have ability to use structure information and abundant fine-grained features. Furthermore, through ensemble learning method, our proposed MGML-FENets can obtain more convincing final predictions. Extensive classification experiments on multiple RS datasets (AID, NWPU-RESISC45, UC-Merced and VGoogle) demonstrate that our proposed networks achieve better performance than previous state-of-the-art (SOTA) networks. The visualization analysis also shows the good interpretability of MGML-FENet.

Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world. Climate change further aggravates this issue. Data-driven deep learning approaches could widen the access to accurate multi-day forecasts, to mitigate against such events. However, there is currently no benchmark dataset dedicated to the study of global precipitation forecasts. In this paper, we introduce \textbf{RainBench}, a new multi-modal benchmark dataset for data-driven precipitation forecasting. It includes simulated satellite data, a selection of relevant meteorological data from the ERA5 reanalysis product, and IMERG precipitation data. We also release \textbf{PyRain}, a library to process large precipitation datasets efficiently. We present an extensive analysis of our novel dataset and establish baseline results for two benchmark medium-range precipitation forecasting tasks. Finally, we discuss existing data-driven weather forecasting methodologies and suggest future research avenues.