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This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

Heavy rainfall is one of the main causes of the degradation of historic rammed Earth architecture. For this reason, ensuring the conservation thereof entails understanding the factors involved in these risk situations. The purpose of this study is to research three past events in which rainfall caused damage and collapse to historic rammed Earth fortifications in Andalusia in order to analyse whether it is possible to prevent similar situations from occurring in the future.

The three case studies analysed are located in the south of Spain and occurred between 2017 and 2021. The hazard presented by rainfall within this context has been obtained from Art-Risk 3.0 (Registration No. 201999906530090). The vulnerability of the structures has been assessed with the Art-Risk 1 model. To characterise the strength, duration, and intensity of precipitation events, a workflow for the statistical use of GPM and GSMaP satellite resources has been designed, validated, and tested. The strength of the winds has been evaluated from data from ground-based weather stations.

GSMaP precipitation data is very similar to data from ground-based weather stations. Regarding the three risk events analysed, although they occurred in areas with a torrential rainfall hazard, the damage was caused by non-intense rainfall that did not exceed 5 mm/hour. The continuation of the rainfall for several days and the poor state of conservation of the walls seem to be the factors that triggered the collapses that fundamentally affected the restoration mortars.

A workflow applied to vulnerability and hazard analysis is presented, which validates the large-scale use of satellite images for past and present monitoring of heritage structure risk situations due to rain.

We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images. VGI data is often provided only as vector data represented by lines and not as full extent. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. The method consists of image segmentation and traversal of multiple agents along available VGI information. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images.

As stated in the United Nations Global Assessment Report 2022 Concept Note, decision-makers everywhere need data and statistics that are accurate, timely, sufficiently disaggregated, relevant, accessible and easy to use. The purpose of this paper is to demonstrate scalable and replicable methods to advance and integrate the use of earth observation (EO), specifically ongoing efforts within the Group on Earth Observations (GEO) Work Programme and the Committee on Earth Observation Satellites (CEOS) Work Plan, to support risk-informed decision-making, based on documented national and subnational needs and requirements.

Promotion of open data sharing and geospatial technology solutions at national and subnational scales encourages the accelerated implementation of successful EO applications. These solutions may also be linked to specific Sendai Framework for Disaster Risk Reduction (DRR) 2015–2030 Global Targets that provide trusted answers to risk-oriented decision frameworks, as well as critical synergies between the Sendai Framework and the 2030 Agenda for Sustainable Development. This paper provides examples of these efforts in the form of platforms and knowledge hubs that leverage latest developments in analysis ready data and support evidence-based DRR measures.

The climate crisis is forcing countries to face unprecedented frequency and severity of disasters. At the same time, there are growing demands to respond to policy at the national and international level. EOs offer insights and intelligence for evidence-based policy development and decision-making to support key aspects of the Sendai Framework. The GEO DRR Working Group and CEOS Working Group Disasters are ideally placed to help national government agencies, particularly national Sendai focal points to learn more about EOs and understand their role in supporting DRR.

The unique perspective of EOs provide unrealized value to decision-makers addressing DRR. This paper highlights tangible methods and practices that leverage free and open source EO insights that can benefit all DRR practitioners.

This study aims to determine shoreline change statistics and net erosion and accretion, along the Kuakata Coast, a magnificent sea beach on Bangladesh’s southernmost point.

The research follows a three stages way to achieve the target. First, this study has used the geographic information system (GIS) and remote sensing (RS) to detect the temporal observation of shoreline change from the year 1991 to 2021 through satellite data. Then, the digital shoreline analysis system (DSAS) has also been explored. What is more, a prediction has been done for 2041 on shoreline shifting scenario. The shoreline displacement measurement was primarily separated into three analytical zones. Several statistical parameters, including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), End Point Rate (EPR) and Linear Regression Rate (LRR) were calculated in the DSAS to quantify the rates of coastline movement with regard to erosion and deposition.

EPR and LRR techniques revealed that the coastline is undergoing a shift of landward (erosion) by a median rate of 3.15 m/yr and 3.17 m/yr, respectively, from 1991 to 2021, 2.85 km² of land was lost. Naval and climatic influences are the key reasons for this variation. This study identifies the locations of a significantly eroded zone in Kuakata from 1991 to 2021. It highlights the places that require special consideration while creating a zoning plan or other structural design.

This research demonstrates the spatio-temporal pattern of the shoreline location of the Kuakata beach, which would be advantageous for the region’s shore management and planning due to the impacts on the fishing industry, recreation and resource extraction. Moreover, the present research will be supportive of shoreline vulnerability. Hence, this study will suggest to the local coastal managers and decision-makers for particularizing the coastal management plans in Kuakata coast zone.

In the past decade, in the space industry, many initiatives intended at offering open access to big data from space multiplied. Therefore, firms started adopting business models (BMs) which lever on digital technologies (e.g. cloud computing, high-performance computing and artificial intelligence), to seize these opportunities. Within this scenario, this article aims at answering the following research question: which digital technologies do impact which components the BM is made of?

An exploratory multiple case study approach was used. Three cases operating in the space industry that lever on digital technologies to implement their business were analyzed. Despite concerns regarding reliability and validity, multiple case studies allow greater understanding of causality, and show superiority respect to quantitative studies for theory building.

Big data, system integration (artificial intelligence, high-performance computing) and cloud computing seem to be pivotal in the space industry. It emerges that digital technologies involve all the different areas and components of the BM.

This paper sheds light on the impact that digital technologies have on the different BM components. It is only understanding which technologies can support the value proposition, which technologies make the infrastructural part able to support this proposition, which technologies may be helpful for delivering and communicating this value to customers and which technologies may help firms to appropriate the value that it is possible to seize the impact of digital technologies on BM.

The purpose of the study is to evaluate whether nighttime luminosity sourced from the Defense Meteorological Satellite Program-Operational Linescan System satellite sensors is a suitable proxy for measuring poverty in Africa.

Our study performs wavelet coherence analysis to investigate the time-frequency synchronization between the nightlight data and “income-to-wealth” ratio for 39 African countries between 1992 and 2012.

All-in-all, the authors find that approximately a third of African countries produce positive synchronizations between nighttime data and “income-to-wealth” ratio and hence conclude that most African countries are not at liberty to use nighttime data to proxy conventional poverty statistics.

In differing from previous studies, the authors examine the suitability of nightlight intensity as a proxy of poverty for individual African countries using much more rigorous analysis.

Myanmar has an insufficient number of mental health workers with few institutional facilities resulting in a significant treatment gap. Although few mental health services are integrated into primary health care (PHC), the challenges are unknown. This study aimed to assess the challenges perceived by providers in the service delivery of satellite mental health care (SMHC) in two sample townships in Yangon.

The research was based on a case study design by applying a qualitative approach using in-depth interviews (IDIs). In the three types of service providers, a total of six staff participated as interviewees. These consisted of two team leaders, two clinical specialists providing consultations to clients and two mental health nurses.

Providers perceived the following as major challenges in the provision of services: unstable financial resources and management, insufficient human resources and capacity of service providers, restricted outpatient services, the lack of a functional referral system, overcrowding, inadequate individual consultation time, long-waiting hours, finite opening days and hours and poor setting of infrastructure, resulting in lack of privacy.

In the absence of similar studies in Myanmar, findings could not be placed in the context of the national literature for comparison. Further, the study involved a limited number of respondents, which may have affected the findings.

Although the challenges revealed were not uncommon in mental health services in developing countries, this study focused on a specific model of mental health care integrated into general healthcare settings in Myanmar. The findings offer a benchmark on efforts to develop decentralized mental health services in Myanmar and provide input for future in-depth studies.

New Space activities offer benefits for human progress and life beyond the Earth. However, there is a risk that the New Space Economy may develop according to an anthropocentric mindset favouring human progress and survival at the expense of all other species and the environment. This mindset raises concerns over the social and environmental impacts of space activities and the accountability of space actors. This research article explores the accountability of space actors by presenting a pluralistic accountability framework to understand, inspire and change accountability in the New Space Economy. This study also identifies future research opportunities.

This paper is a reflective and normative essay. The arguments are developed using contemporary multidisciplinary academic literature, publicly available evidence and examples. Further, the authors use Dillard and Vinnari's accountability framework to examine a pluralistic accountability system for space businesses.

The New Space Economy requires public and private entities to embrace hybrid and pluralistic accountability for their social and environmental impacts. A new way of seeing the relationship between human life, the Earth and celestial space is needed. Accounting language is used to mirror and mobilise broader forms of responsibility in those involved in space.

This paper responds to the AAAJ's special issue call for examining how accountability can be ensured in the New Space Age. The space activities businesses conduct, and the anthropocentric view inspiring their race toward space is concerning. Hence, the authors advocate the need for rethinking accountability between humans and nature. The paper contributes to fostering the debate on social and environmental accounting and the accountability of space actors in the New Space Economy. To this end, the authors use a pluralistic accountability framework to help understand how the New Space Economy can face the risks emanating from its anthropocentric mindset.

Carbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.

This paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO₂ and PM_2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.

Results suggest that the carbon trading mechanism induces 2.7% of CO₂ emission reduction and 6.7% of PM_2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.

This paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.