Information about buildings and building change is essential for many practical applications, especially for humanitarian emergency response in temporary settlement areas hosting forcibly displaced people. In a classical building change detection approach, pairs of pre- and post-images and corresponding annotations indicating building change are required. This approach has two critical limitations. First, it is inefficient in time-sensitive applications like emergency response to create annotations, which is constrained by the availability of post- and pre-event images and is hard to create sufficient annotations in a limited response time. Second, even under normal circumstances, changed objects could be rare and getting sufficient change examples to train a change detection model is a challenging task. We followed a generative approach to generate synthetic bi-temporal images from a single-time image to train a change detection model. Our results show that the generative models employed managed to generate bi-temporal images with structural and style similarity. A change detection model trained on the generated synthetic dataset has achieved a mAP ranging from 4.7% to 42.6% without accessing real bi-temporal building change annotations.

The era of Artificial Intelligence ‘AI’ with all the benefits brought along, has raised new and additional challenges to the ongoing efforts of assessing, defining, formulating, and implementing the quality aspects of geospatial remote sensing data. Developed practices using artificial intelligence leveraged techniques such as image interpretation, classification, thematic mapping, and even image quality enhancement, necessitating by that the reassessment and redevelopment of some of the related emerging quality aspects. Moreover, technology also made the generation of false images and false data possible, this matter constrained and increased precaution and doubtfulness, altogether making some practices based on that data almost halt to further notice.

This paper presents the collaborative research work to assess and clarify the quality aspects that arose with the advent and implementation of AI and associated technologies; the concerns and issues that can accompany the generation of false satellite and aerial images including the generated geospatial data out of which, how the new emerged quality aspects fit into the currently existing methods through the lifecycle of remote sensing data production and usage, and consequently how the quality dimensions are affected and should be further developed and improved to tackle the changes and innovations. Also, lame a bit on investigating how to accommodate the new challenges in standards, and practical procedures and raise the awareness to users, the level of dependency on improved and enhanced satellite images when it comes to data collection interpretation and classification, and finally define the research gaps, future expected challenges and thus enclose suggestions and recommendations in that respect.

Cultural property includes immovable assets that are part of a nation’s cultural heritage and reflect the cultural identity of a people. Hence, information about armed conflict’s impact on historical buildings’ structures and heritage sites is extremely important. The study aims to demonstrate the application of Earth observation (EO) synthetic aperture radar (SAR) technology, and in particular Sentinel-1 SAR coherence time-series analysis, to monitor spatial and temporal changes related to the recent Russian–Ukrainian war in the urban areas of Mariupol and Kharkiv, Ukraine. The study considers key events during the siege of Mariupol and the battle of Kharkiv from February to May 2022. Built-up areas and cultural property were identified using freely available OpenStreetMap (OSM) data. Semi-automated coherent change-detection technique (CCD) that utilize difference analysis of pre- and co-conflict coherences were capable of highlighting areas of major impact on the urban structures. The study applied a logistic regression model (LRM) for the discrimination of damaged and undamaged buildings based on an estimated likelihood of damage occurrence. A good agreement was observed with the reference data provided by the United Nations Satellite Centre (UNOSAT) in terms of the overall extent of damage. Damage maps enable the localization of buildings and cultural assets in areas with a high probability of damage and can serve as the basis for a high-resolution follow-up investigation. The study reveals the benefits of Sentinel-1 SAR CCD in the sense of unsupervised delineation of areas affected by armed conflict. However, limitations arise in the detection of local and single-building damage compared to regions with large-scale destruction. The proposed semi-automated multi-temporal Sentinel-1 data analysis using CCD methodology shows its applicability for the timely investigation of damage to buildings and cultural heritage, which can support the response to crises.

Updated building footprints with refugee camps from high-resolution satellite imagery can support related humanitarian operations. This study explores the utilization of the “Segment Anything Model” (SAM) and one of its branches, SAM-Adapter, for semantic segmentation tasks in the building extraction from satellite imagery. SAM-Adapter is a lightweight adaptation of the SAM and emerges as a powerful tool for this extraction task across diverse refugee camps. Our research proves that SAM-Adapter excels in scenarios where data availability is limited compared to other classic (e.g., U-Net) or advanced semantic segmentation models (e.g., Transformer). Furthermore, the impact of upscaling techniques on model performance is highlighted, with methods like super-resolution (SR) models proving invaluable for improving model performance. Additionally, the study unveils intriguing phenomena, including the model’s rapid convergence in the first training epoch when using upscaled image data for training, suggesting opportunities for future research. The codes covering each step from data preparation, model training, model inferencing, and the generation of Shapefiles for predicted masks are available on a GitHub repository to benefit the extended scientific community and humanitarian operations.

Detecting building changes using earth observation datasets has numerous applications, particularly in humanitarian emergency responses. Deep learning models for change detection have shown considerable progress, but they require large annotated datasets for training and validation. Although semi-supervised change detection methods utilize a large amount of unlabeled data from the same domain, their effectiveness is limited when applied across different datasets due to shifts in data distribution. We proposed a cross-domain semi-supervised change detection approach that incorporates consistency regularization and a deep change domain mapping strategy to handle data distribution shifts. This method achieved a change class Intersection over Union (IoU) of 0.57 and 0.22 in the challenging cross-domain scenarios of LEVIR-CD → WUHAN-CD and LEVIR-CD → EGY-BCD open datasets, respectively. Additionally, we tested this approach for monitoring building changes in temporary settlements hosting forcibly displaced people (FDP), using cross-domain data from open datasets and images of these temporary settlements. Despite the improvements introduced by domain mapping, the results are not yet sufficient for practical use in emergency response operations.

CustomizingSegment Anything Model(SAM)hasrecentlyat-tractedconsiderableattention in remote sensing domains.This studyex-plores the performance of SAM-Adapter in refugee-dwelling extractioninthree different refugee camps from high-resolution satelliteimages.Thefindings indicate that with scarce sample data, SAM-Adaptermarginallyoutperforms other semantic segmentation models. This underscores SAM’spromising potential forbuilding extraction tasks when data is limited.

Even though computer vision models are excellent for automatic scene segmentation and object identification from remotely sensed imagery, they demand a huge corpus of annotated data for the training and validation which is a huge challenge in humanitarian emergency response. To tackle this problem, we propose unsupervised dwelling object counting combining Variational Autoencoder (VAE) with an anomaly detection approach. The approach is tested in six Forcibly Displaced People (FDP) settlement areas situated in different parts of the world. Using an anomaly map computed with the VAE model, we demonstrated the possibility of properly locating dwelling objects using anomaly maps. Dwelling counts are obtained by further segmenting anomaly maps. Results show that, though it has strong spatio-temporal variation, the VAE model exhibits promising potential for locating and counting dwellings. It is also observed that in FDP settlements with dense buildings and extremely low contrast between buildings and ground or environment, the performance is relatively lower than the performance achieved in settlement areas with regularly spaced and less complex building structures.

Dwelling information is essential for humanitarian emergency response during or in the aftermath of disasters, especially in temporary settlement areas hosting forcibly displaced people. To map dwellings, the integration of very high-resolution remotely sensed imagery in computer vision models plays a key role. However, state-of-the-art deep learning models have two known downsides: 1) lack of generalization across space and time under changing scenes and object characteristics, and 2) extensive demand for annotated samples for training and validation. Both could pose a critical challenge during an emergency. To bypass this problem, this study deals with unsupervised domain adaptation for instance segmentation using a single-stage instance segmentation model, namely segmenting objects by location (SOLO). The goal is to adapt a SOLO model trained on a labeled source domain to detect dwellings in an unlabeled target domain. In this context, we study three domain adaptation techniques based on adversarial learning, domain discrepancy, and domain alignment mapping. We also propose domain similarity at different levels to understand its implication on domain adaptation. Experiments are conducted on very high-resolution satellite images obtained from four temporary settlement areas located in different countries and exhibiting various spatial characteristics. Analysis results show that in most source–target combinations unsupervised domain adaptation improves the performance by a large margin even surpassing a model trained with supervised learning. There is also an observed performance deviation among implemented strategies and different source–target dataset combinations. From the in-depth analysis of domain similarity at the image, object, and deep feature space levels, the former is more correlated with unsupervised domain adaptation performance.

Dwelling information is very important for various applications in humanitarian emergency response. For this, Earth observation is crucial to have spatially explicit and temporally frequent observations. Coupled with advances in computer vision, especially with the proliferation of state-of-the-art deep learning models, are providing a new opportunity for automatic information retrieval from remotely sensed imagery. Despite their proven performance, they have two known limitations, viz, the requirement of intensive data for training and lack of universal generalization under changing scene characteristics and respective data distributions. To tackle this problem, the current study has investigated the relevance of a meta-learning approach for the creation of a spatially transferable optimal model for dwelling extraction in IDP/refugee settlements. The study followed a Model Agnostic meta-Learning (MAML) strategy with newly designed and tested variates with weighted loss gradient update plus self-supervision in the adaptation phase to the target locations using a few samples. The approach is tested using multi-sensor, multi-temporal satellite imagery from eight IDP settlements. Furthermore, a thorough investigation is undertaken on task-specific transfers and their association with deep-embedded feature space and image structural similarity. Results indicate that for some target sites, task-specific transfers perform better than MAML approaches. When MAML is trained with a weighted loss gradient update, it yielded better performance. The best performance (MIoU 0.623 and an F-1 score of 76.7 %) was achieved when MAML is aided with self-supervision using pseudo-labels from unlabelled target data. In all experimental setups, though increasing adaptation samples contribute to positive transfer, the marginal contribution from additional samples is decreasing and stagnates when the adaptation sample size reaches ∼ 35 % of the target dataset.

Even though computer vision models are excellent for automatic scene segmentation and object identification from remotely sensed imagery, they demand a huge corpus of annotated data for the training and validation which is a huge challenge in humanitarian emergency response. To tackle this problem, we propose unsupervised dwelling object counting combining Variational Autoencoder (VAE) with an anomaly detection approach. The approach is tested in six Forcibly Displaced People (FDP) settlement areas situated in different parts of the world. Using an anomaly map computed with the VAE model, we demonstrated the possibility of properly locating dwelling objects using anomaly maps. Dwelling counts are obtained by further segmenting anomaly maps. Results show that, though it has strong spatio-temporal variation, the VAE model exhibits promising potential for locating and counting dwellings. It is also observed that in FDP settlements with dense buildings and extremely low contrast between buildings and ground or environment, the performance is relatively lower than the performance achieved in settlement areas with regularly spaced and less complex building structures.

As the climate crisis continues to worsen, there is an increasing demand for scientific evidence from Climate Risk and Vulnerability Assessments (CRVA). We present 12 methodological advancements to the Impact Chain-based CRVA (IC-based CRVA) framework, which combines participatory and data-driven approaches to identify and measure climate risks in complex socio-ecological systems. The advancements improve the framework along five axes, including the existing workflow, stakeholder engagement, uncertainty management, socio-economic scenario modeling, and transboundary climate risk examination. Eleven case studies were conducted and evaluated to produce these advancements. Our paper addresses two key research questions: a) How can the IC-based CRVA framework be methodologically advanced to produce more accurate and insightful results? and b) How effectively can the framework be applied in research and policy domains that it was not initially designed for? We propose methodological advancements to capture dynamics between risk factors, to resolve contradictory worldviews, and to maintain consistency between Impact Chains across policy scales. We suggest using scenario-planning techniques and integrating uncertainties via Probability Density Functions and Reverse Geometric Aggregation. Our research examines the applicability of IC-based CRVAs to address transboundary climate risks and integrating macro-economic models to reflect possible future socio-economic exposure. Our findings demonstrate that the modular structure of IC-based CRVA allows for the integration of various methodological advancements, and further advancements are possible to better assess complex climate risks and improve adaptation decision-making.

The increasing number of satellites offering daily image acquisition coverage at very high resolution (VHR) provides new insights into the dynamics of fine-scale changes at temporary settlements. This article presents first experiences with spaceborne mapping of refugee settlements using daily revisiting radar (ICEYE) and optical (PlanetScope) imagery of a study area in the Northeastern Democratic Republic of Congo, which hosts villages and informal dwellings of forcibly displaced persons. The information content of the sensors is systematically compared with respect to mapping land use, delineating built-up areas, and identifying camps. Validation against independent reference data shows many of the lightweight dwellings to not be included in the radar image due to the use of natural construction materials. The best distinctions between built-up areas and bare soil was achieved upon merging optical and radar data, resulting in a classification accuracy of 94.1%. The classification is based on four predefined classes, and more accurately identifies urban structures compared to the two-class approach that either underestimates or overestimates built-up areas. Accuracies between 83.4 % and 92.8 % have been achieved for 10 identified campsites. The results are a promising step toward operationally identifying refugee settlements for emergency response.

This research assessed the influences of four band combinations and three types of pretrained weights on the performance of semantic segmentation in extracting refugee dwelling footprints of the Kule refugee camp in Ethiopia during a dry season and a wet season from very high spatial resolution imagery. We chose a classical network, U-Net with VGG16 as a backbone, for all segmentation experiments. The selected band combinations include 1) RGBN (Red, Green, Blue, and Near Infrared), 2) RGB, 3) RGN, and 4) RNB. The three types of pretrained weights are 1) randomly initialized weights, 2) pretrained weights from ImageNet, and 3) weights pretrained on data from the Bria refugee camp in the Central African Republic). The results turn out that three-band combinations outperform RGBN bands across all types of weights and seasons. Replacing the B or G band with the N band can improve the performance in extracting dwellings during the wet season but cannot bring improvement to the dry season in general. Pretrained weights from ImageNet achieve the best performance. Weights pretrained on data from the Bria refugee camp produced the lowest IoU and Recall values.

Refugee-dwelling footprints derived from satellite imagery are beneficial for humanitarian operations. Recently, deep learning approaches have attracted much attention in this domain. However, most refugees are hosted by low- and middle-income countries where accurate label data are often unavailable. The Object-Based Image Analysis (OBIA) approach has been widely applied to this task for humanitarian operations over the last decade. However, the footprints were usually produced urgently, and thus, include delineation errors. Thus far, no research discusses whether these footprints generated by the OBIA approach (OBIA labels) can replace manually annotated labels (Manual labels) for this task. This research compares the performance of OBIA labels and Manual labels under multiple strategies by semantic segmentation. The results reveal that the OBIA labels can produce IoU values greater than 0.5, which can produce applicable results for humanitarian operations. Most falsely predicted pixels source from the boundary of the built-up structures, the occlusion of trees, and the structures with complicated ontology. In addition, we found that using a small number of Manual labels to fine-tune models initially trained with OBIA labels can outperform models trained with purely Manual labels. These findings show high values of the OBIA labels for deep-learning-based refugee-dwelling extraction tasks for future humanitarian operations.

Extracting footprints of refugee dwellings from satellite imagery supports dedicated humanitarian operations. Recently, deep-learning-based approaches have been proved to be effective for this task. However, such research is still limited due to the lack of cleaned labels for supervised-learning-based models. This research compares the performance of noisy labels from past humanitarian operations and cleaned labels by manual annotation in three classical deep learning architectures (U-Net, LinkNet and Feature Pyramid Network (FPN)) and twelve backbones (VGG16, VGG19, ResNet-18, ResNet-34, DenseNet-121, DenseNet-169, Inception-v3, InceptionResnet-v2, MobileNet-v1, MobileNet-v2, EfficientNet-B0, EfficientNet-B1). The results turn out that even though cleaned labels outperform noisy labels, the noisy labels have a high potential to replace cleaned labels because producing cleaned labels requires much more time, and predicted footprints of models trained with noisy labels are promising in humanitarian applications. Besides, the performance of the selected architectures and backbones is similar in general. Overall, FPN with VGG16, LinkNet with DenseNet-121, and U-Net with EfficientNet-B0 outperform other combinations. If considering both accuracy and training time, U-Net with VGG16 and LinkNet with ResNet-18 could be two alternatives for future research.

The Boko Haram Insurgency, a religious conflict in northeastern Nigeria, has caused around 350000 deaths and destroyed numerous populated sites in the past decade. This research used Sentinel-2 data and point data of populated sites with their status information (Functional or Destroyed) collected by Copernicus Emergency Management Service as input to train an image classification model, EfficientNet, to detect the status of the populated sites in this region. The research tested the influence of two hyperparameters (patchsize and the number of neurons in a fully connected layer) and two types of preprocessed imagery data to explore the potential of this approach for this detecting task. The results turn out the developed approach produces the highest F1-score of 0.8197 for destroyed populated sites when the model is trained with NDVI data, the patchsize is 1100m, and the number of FC neurons is 512. The results prove this approach has a high potential for long-term peace monitoring and humanitarian operations in this region.

Refugee-dwelling footprints derived from satellite imagery are beneficial for humanitarian operations. Recently, deep learning approaches have attracted much attention in this domain. However, most refugees are hosted by low- and middle-income countries where accurate label data are often unavailable. The Object-Based Image Analysis (OBIA) approach has been widely applied to this task for humanitarian operations over the last decade. However, the footprints were usually produced urgently, and thus, include delineation errors. Thus far, no research discusses whether these footprints generated by the OBIA approach (OBIA labels) can replace manually annotated labels (Manual labels) for this task. This research compares the performance of OBIA labels and Manual labels under multiple strategies by semantic segmentation. The results reveal that the OBIA labels can produce IoU values greater than 0.5, which can produce applicable results for humanitarian operations. Most falsely predicted pixels source from the boundary of the built-up structures, the occlusion of trees, and the structures with complicated ontology. In addition, we found that using a small number of Manual labels to fine-tune models initially trained with OBIA labels can outperform models trained with purely Manual labels. These findings show high values of the OBIA labels for deep-learning-based refugee-dwelling extraction tasks for future humanitarian operations.

Earth-observation-based mapping plays a critical role in humanitarian responses by providing timely and accurate information in inaccessible areas, or in situations where frequent updates and monitoring are required, such as in internally displaced population (IDP)/refugee settlements. Manual information extraction pipelines are slow and resource inefficient. Advances in deep learning, especially convolutional neural networks (CNNs), are providing state-of-the-art possibilities for automation in information extraction. This study investigates a deep convolutional neural network-based Mask R-CNN model for dwelling extractions in IDP/refugee settlements. The study uses a time series of very high-resolution satellite images from WorldView-2 and WorldView-3. The model was trained with transfer learning through domain adaptation from nonremote sensing tasks. The capability of a model trained on historical images to detect dwelling features on completely unseen newly obtained images through temporal transfer was investigated. The results show that transfer learning provides better performance than training the model from scratch, with an MIoU range of 4.5 to 15.3%, and a range of 18.6 to 25.6% for the overall quality of the extracted dwellings, which varied on the bases of the source of the pretrained weight and the input image. Once it was trained on historical images, the model achieved 62.9, 89.3, and 77% for the object-based mean intersection over union (MIoU), completeness, and quality metrics, respectively, on completely unseen images.

Shifting from effect-oriented toward cause-oriented and systemic approaches in sustainable climate change adaptation requires a solid understanding of the climate-related and societal causes behind climate risks. Thus, capturing, systemizing, and prioritizing factors contributing to climate risks are essential for developing cause-oriented climate risk and vulnerability assessments (CRVA). Impact chains (IC) are conceptual models used to capture hazard, vulnerability, and exposure factors that lead to a specific risk. IC modeling includes a participatory stakeholder phase and an operational quantification phase. Although ICs are widely implemented to systematically capture risk processes, they still show methodological gaps concerning, for example, the integration of dynamic feedback or balanced stakeholder involvement. Such gaps usually only become apparent in practical applications, and there is currently no systematic perspective on common challenges and methodological needs. Therefore, we reviewed 47 articles applying IC and similar CRVA methods that consider the cause–effect dynamics governing risk. We provide an overview of common challenges and opportunities as a roadmap for future improvements. We conclude that IC should move from a linear-like to an impact web–like representation of risk to integrate cause–effect dynamics. Qualitative approaches are based on significant stakeholder involvement to capture expert-, place-, and context-specific knowledge. The integration of IC into quantifiable, executable models is still highly underexplored because of a limited understanding of systems, data, evaluation options, and other uncertainties. Ultimately, using IC to capture the underlying complex processes behind risk supports effective, long-term, and sustainable climate change adaptation.

Current scientific discourse on the assessment of loss and damage from climate change focuses primarily on what is straightforwardly quantifiable, such as monetary value, numbers of casualties, or destroyed homes. However, the range of possible harms induced by climate change is much broader, particularly as regards residual risks that occur beyond limits to adaptation. In international climate policy, this has been institutionalized within the Loss and Damage discourse, which emphasizes the importance of non-economic loss and damage (NELD). Nevertheless, NELDs are often neglected in loss and damage assessments, being intangible and difficult to quantify. As a consequence, to date, no systematic concept or indicator framework exists that integrates market-based and non-market-based loss and damage. In this perspective, we suggest assessing risk of loss and damage using a climate change risk and vulnerability assessment (CRVA) framework: the Impact Chain method. This highly adaptable method has proven successful in unraveling complex risks in socio-ecological systems through a combination of engaging (political) stakeholders and performing quantitative data analysis. We suggest expanding the framework’s logic to include not only the sources but also the consequences of risk by conceptualizing loss and damage as harm to nine domains of human well-being. Our approach is consistent with the risk conceptualization by the Intergovernmental Panel on Climate Change (IPCC). Conceptualization and systematic assessment of the full spectrum of imminent loss and damage allows a more comprehensive anticipation of potential impacts on human well-being, identifying vulnerable groups and providing essential evidence for transformative and comprehensive climate risk management.

While many studies exist to identify buildings from optical satellite images, radar-based approaches are still lacking in humanitarian contexts. This article outlines the main challenges related to scattering mechanisms returning from huts, tents, informal dwellings, and their natural surroundings, but also from geometric distortions caused by the sidelooking radar aperture. An outlook summarizes how these limitations can be overcome by image enhancement or multi-image composites, but also by advanced methods on building extraction, such as convolutional neural networks (CNNs). This article aims to stimulate scientific debate and to lay a foundation for the development of new methods.

For effective humanitarian response in refugee camps, reliable information concerning dwelling type, extent, surrounding infrastructure, and respective population size is essential. As refugee camps are inherently dynamic in nature, continuous updating and frequent monitoring is time and resource-demanding, so that automatic information extraction strategies are very useful. In this ongoing research, we used labelled data and highresolution Worldview imagery and first trained a Convolutional Neural Network-based U-net model architecture. We first trained and tested the model from scratch for Al Hol camp in Syria. We then tested the transferability of the model by testing its performance in an image of a refugee camp situated in Cameroon. We were using patch size 32, at the Syrian test site, a Mean Area Intersection Over Union (MIoU) of 0.78 and F-1 score of 0.96, while in the transfer site, MIoU of 0.69 and an F-1 score of 0.98 were achieved. Furthermore, the effect of patch size and the combination of samples from test and transfer sites are investigated.

For the generation of 3D city models from satellite stereo imagery beyond the generation of digital surface models (DSM) from stereo data the next crucial step is the separation of urban 3D objects from ground. To do this the most common method is the derivation of a so called digital terrain model (DTM) from the DSM. The DTM should ideally contain only the surface of the ground on which the urban objects are located. Since only the surface of the objects can be seen from space, sophisticated methods have to be developed to gain information of the bare ground. In this paper selected methods for the extraction of a DTM from a DSM are described and evaluated. The evaluation is done by applying the methods to synthetically generated DSMs. These synthetical DSMs are a combination of ground and typical urban objects put on top of it. The application of the DTM extraction methods should recover in turn the original ground model as good as possible. Also the sum of the obtained DTM and the profile of the urban objects should reconstruct the original DSM. The profile of the urban objects ist often referenced as normalized digital elevation model (nDEM). But in general the equation DSM = DTM + nDEM is not always valid – especially for buildings situated on the slope of a hill. If the nDEM would simply be the difference of DSM – DTM the slope of the hill – contained in the DTM – will be reflected on the roof of the buildings. So also an advanced method for derivation of the nDEM from DSM and DTM is presented and tested.

Amongst the many benefits of remote sensing techniques in disaster- or conflict-related applications, timeliness and objectivity may be the most critical assets. Recently, increasing sensor quality and data availability have shifted the attention more towards the information extraction process itself. With promising results obtained by deep learning (DL), the notion arises that DL is not agnostic to input errors or biases introduced, in particular in sample-scarce situations. The present work seeks to understand the influence of different sample quality aspects propagating through network layers in automated image analysis. In this paper, we broadly discuss the conceptualisation of such a sample database in an early stage of realisation: (1) inherited properties (quality parameters of the underlying image such as cloud cover, seasonality, etc.); (2) individual (i.e., per-sample) properties, including a. lineage and provenance, b. geometric properties (size, orientation, shape), c. spectral features (standardized colour code); (3) context-related properties (arrangement Several hundred samples collected from different camp settings were hand-selected and annotated with computed features in an initial stage. The supervised annotation routine is automated so that thousands of existing samples can be labelled with this extended feature set. This should better condition the subsequent DL tasks in a hybrid AI approach.

[Best Paper Award of the GIScience 2021 Conference]

GIScience conference authors and researchers face the same computational reproducibility challenges as authors and researchers from other disciplines who use computers to analyse data. Here, to assess the reproducibility of GIScience research, we apply a rubric for assessing the reproducibility of 75 conference papers published at the GIScience conference series in the years 2012-2018. Since the rubric and process were previously applied to the publications of the AGILE conference series, this paper itself is an attempt to replicate that analysis, however going beyond the previous work by evaluating and discussing proposed measures to improve reproducibility in the specific context of the GIScience conference series. The results of the GIScience paper assessment are in line with previous findings: although descriptions of workflows and the inclusion of the data and software suffice to explain the presented work, in most published papers they do not allow a third party to reproduce the results and findings with a reasonable effort. We summarise and adapt previous recommendations for improving this situation and propose the GIScience community to start a broad discussion on the reusability, quality, and openness of its research. Further, we critically reflect on the process of assessing paper reproducibility, and provide suggestions for improving future assessments.

Sanitation refers to the provision of facilities and services for the safe disposal of human excrements and is included in SDG 6 “Ensuring the availability and sustainable management of water and sanitation for all”. With more than 2.5 billion people without appropriate facilities mainly living in cities, urban sanitation is critical to avoid harmful effects on human health and environment. Largely relying on onsite sanitation, the coordination of safe collection, treatment and disposal is essential. This requires reliable, up-to-date information that can be provided by EO data through its wide availability and objectivity. This is of particular importance in areas difficult to access and large, growing cities with urban sprawl. To facilitate the planning of faecal sludge management (FSM), priority areas where improvements are urgently needed can be identified using EO-based indicators, additionally enabling regular monitoring, evaluation of measures and targeted in-situ sampling.