Measuring and attributing greenhouse gas (GHG) emissions remains a challenging problem as the world strives toward meeting emissions reductions targets. As a significant portion of total global emissions, the road transportation sector represents an enormous challenge for estimating and tracking emissions at a global scale. To meet this challenge, we have developed a hybrid approach for estimating road transportation emissions that combines the strengths of machine learning and satellite imagery with localized emissions factors data to create an accurate, globally scalable, and easily configurable GHG monitoring framework.

Atmospheric processes involve both space and time. Thus, humans looking at atmospheric imagery can often spot important signals in an animated loop of an image sequence not apparent in an individual (static) image. Utilizing such signals with automated algorithms requires the ability to identify complex spatiotemporal patterns in image sequences. That is a very challenging task due to the endless possibilities of patterns in both space and time. Here, we review different concepts and techniques that are useful to extract spatiotemporal signals from meteorological image sequences to expand the effectiveness of AI algorithms for classification and prediction tasks. We first present two applications that motivate the need for these approaches in meteorology, namely the detection of convection from satellite imagery and solar forecasting. Then we provide an overview of concepts and techniques that are helpful for the interpretation of meteorological image sequences, such as (a) feature engineering methods using (i) meteorological knowledge, (ii) classic image processing, (iii) harmonic analysis, and (iv) topological data analysis; (b) ways to use convolutional neural networks for this purpose with emphasis on discussing different convolution filters (2D/3D/LSTM-convolution); and (c) a brief survey of several other concepts, including the concept of “attention” in neural networks and its utility for the interpretation of image sequences and strategies from self-supervised and transfer learning to reduce the need for large labeled datasets. We hope that presenting an overview of these tools—many of which are not new but underutilized in this context—will accelerate progress in this area.

Recent years have seen the rapid expansion of airborne and spaceborne remote-sensing products adopted by archaeologists for interpreting ancient landscapes and managing heritage resources. A growing and increasingly specialized literature attests to the promise and availability of commercial and publicly funded satellite imagery, as well as UAV-mounted sensors across a range of resolutions and price points. In the South Caucasus (including the countries of Armenia, Azerbaijan, and Georgia), a growing commitment to landscape approaches in archaeology is stimulating the adoption of satellite remote sensing as an important new tool for identifying and managing archaeological resources while tracing the impact of historic land-use alterations in survey areas. Nevertheless, budgetary challenges and a lack of training opportunities among international partners and heritage organizations outside of the funding streams of large academic institutions can lead to widening technological gulfs in the discipline that reinforce colonial relationships. Building on recent technical articles covering specific imagery datasets, this article aims to address this by providing a general review of free or low-cost remotely sensed datasets available to archaeologists, with the aim of broadening awareness of these important tools and their vocabularies, and illustrating them with recent published examples from the South Caucasus.

Nearly 500 kite-like structures have recently been discovered in the western Sahara using high resolution satellite images. Although four distinct types with different morphologies have been distinguished, their characteristics, topographical location and orientation in the landscape indicate that they are all most likely hunting traps.

Barrier islands are sedimentary bodies susceptible to changes in sediment supply, dominant physical processes, and sea level. The aim of this work was to study the sedimentary processes that established Marambaia Barrier Island (SE Brazil) as an elongated sandy body that created Sepetiba Bay. For this purpose, barrier and back-barrier bay environments were analyzed using high-resolution satellite imagery, geophysical and topographic surveys, surface sediment samples and short cores, and radiocarbon and optically stimulated luminescence (OSL) dating techniques. Seven morpho-sedimentary domains were identified: coastal beach ridges, overland flow features, inter-ridge paleo lagoon, bayside beach ridges, marshlands, dune field and tidal wetlands. The results show that Marambaia Barrier Island evolved throughout the Holocene, first under normal regression conditions during sea-level rise, and then by forced regression as sea level lowered to its present position. Concurrent processes related to longshore drift, onshore transport, reworked barrier deposits, eolian transport, bay circulation, and pedogenesis influenced its morpho-sedimentary evolution. Morphological features such as truncated beach ridges, flying spits, and filled channels attest to the occurrence of alternating periods of erosion and accretion, evincing how the morphology of barrier island systems preserves an important archive of environmental changes.

The Batn el-Hagar in Sudan has traditionally been characterised as sparsely occupied during the Middle Kingdom Period, with most activity limited to the Egyptian fortresses along the Second Cataract. A new survey programme undertaken by the Uronarti Regional Archaeological Project offers evidence for a more richly occupied landscape.

High-resolution satellite imagery has proved to be a powerful tool for calculating the extent of looting at heritage sites in conflict zones around the world. Monitoring damage over time, however, has been largely dependent upon laborious and error-prone manual comparisons of satellite imagery taken at different dates. The semi-automated detection process presented here offers a more expedient and accurate method for monitoring looting activities over time, as evidenced at the site of Ai Khanoum in Afghanistan. It is hoped that this method, which relies upon multispectral imagery and principal component analysis, may be adapted to great effect for use in other areas where heritage loss is of significant concern.

A Geographic Information System (GIS) was developed to evaluate its utility in an eradication program of common crupina. Infested areas were recorded in the GIS along with information normally found on United States Geological Survey topographic maps. Infested areas were separated into management areas according to possible treatment method and special environmental or health requirements such as proximity to streams or inhabited buildings. The location, size, and type of each management area were recorded in the database. The database was constructed to maintain treatment and efficacy data for tracking the process of eradication for each infestation. Areas were identified for detection surveying, including coordinates for navigating with a global positioning system (GPS).

The use of plant growth regulators (PGRs) on winter wheat in the UK is a common practice. Significant yield benefits can be gained from its use. For the most efficient use of PGRs the rates of application must be matched to the crop canopy. The variation of crop growth was monitored during the spring growing season using vegetation indices derived from satellite imagery. Application rates of PGRs were matched to changes in biomass as measured by the imagery. Final yields were measured using a GPS combine yield meter. The variable application showed an average yield benefit of 0.4 t/ha over the uniform full recommended application rate.

Since the launch of the first commercial very high resolution satellite sensor in 1999 there has been a growing awareness and application of space technology for the remote identification of potential violations of human rights and international humanitarian law. As examined in the three cases of armed conflict in Gaza, Georgia, and Sri Lanka, analysis of satellite imagery was able to provide investigators with independent, verifiable, and compelling evidence of serious violations of international humanitarian law. Also examined are the important limitations to such imagery-based analysis, including the larger technical, analytical, and political challenges facing the humanitarian and human rights community for conducting satellite-based analysis in the future.

The author describes a process of systematic integration of aerial and satellite imagery, which has provided a huge increase in the number of known burial mounds in the area where the Danube meets the Black Sea. Careful evaluation of newly acquired and archival imagery from satellites and lower level platforms shows where data is comparable and how visibility varies with imagery type. Excavations to date suggest the majority of the mounds are of Greco-Roman date and associated with the large towns and their road networks. Clusters of barrows exist, hinting at associated settlement aggregation, but a large proportion are single tumuli, raising interesting questions about their social role in this period. Above all, the large numbers revealed by the survey must invite new thoughts on whether, or in what way, the mounds reflect social ranking.

High resolution satellite photographs offer a new picture of the tracks along which the Easter Island giant statues were hauled from the central quarry to the exhibition sites. The survey traced 32 km of seven major roads, confirmed by features on the ground, where their current condition gives reasons for concern. The authors suggest that the radial pattern implies social division into small groups.

A previous analysis of tick infestation patterns on rodents in Slovakia suggested that the key to the focal distribution of western-type tick-borne encephalitis virus (TBEv) in Europe is the geographically variable degree of synchrony in the seasonal activity of larval and nymphal Ixodes ricinus ticks. This prediction is here tested by examining records, from 7 different countries, of the seasonal variation in the abundance of larvae and nymphs feeding on rodents or questing on the vegetation. Larvae consistently started feeding and questing earlier in the year at sites within TBEv foci than elsewhere, so that they appeared in the spring as soon as nymphs were active. Such larval–nymphal synchrony is associated with a rapid fall in ground-level temperatures from August to October as revealed by the satellite-derived index of Land Surface Temperature (LST). Likewise, of 1992 pixels sampled on a grid across Europe, the 418 that fell within TBEv foci were characterized by a higher than average rate of autumnal cooling relative to the peak midsummer LST. It is proposed that such a seasonal temperature profile may cause unfed larvae to pass the winter in quiescence, from which they emerge synchronously with nymphs in the spring.