Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data

Informal settlements are home to the most socially and economically vulnerable people on the planet. In order to deliver effective economic and social aid, non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), require detailed maps of the locations of informal settlements. However, data regarding informal and formal settlements is primarily unavailable and if available is often incomplete. This is due, in part, to the cost and complexity of gathering data on a large scale. To address these challenges, we, in this work, provide three contributions. 1) A brand new machine learning data-set, purposely developed for informal settlement detection. 2) We show that it is possible to detect informal settlements using freely available low-resolution (LR) data, in contrast to previous studies that use very-high resolution (VHR) satellite and aerial imagery, something that is cost-prohibitive for NGOs. 3) We demonstrate two effective classification schemes on our curated data set, one that is cost-efficient for NGOs and another that is cost-prohibitive for NGOs, but has additional utility. We integrate these schemes into a semi-automated pipeline that converts either a LR or VHR satellite image into a binary map that encodes the locations of informal settlements.Comment: Published at the AAAI/ACM Conference on AI, ethics and society. Extended results from our previous workshop: arXiv:1812.0081

Sequential Recurrent Encoders for Land Cover Mapping in the Brazilian Amazon using MODIS Imagery and Auxiliary Datasets

To test an existing sequential recurrent encoders model based on convolutional variants of RNNs for the task of LUC classification across the Brazilian Amazon and to compare different arrangements of input features and their impact on the classifier performanc

Efectos de purines de chipaca (bidens pilosa l.) y de microorganismos en la incidencia y severidad de phytophthora infestans (mont.) de bary en papa criolla (solanum phureja) cultivada en tenjo (cundinamarca, colombia) / effects of chipaca (bidens pilosa

En una finca ecológica del municipio de Tenjo(Cundinamarca-Colombia) se estudió el efecto de variosmateriales preparados a partir de chipaca (Bidens pilosa) y deaislamientos microbianos, sobre la incidencia y severidad deP. infestans en un cultivo de papa criolla (Solanum phureja),utilizando un diseño completamente al azar (DCA) consubmuestreo y ocho tratamientos: purín de flores; purín mezcla; actinomiceto AC 12 (Streptomyces sp.); mezcla de actinomicetos; aislamiento bacteriano (Bacillus subtilis); mezcla bacterias (Bacillus subtilis y Burkholderia cepacia); Hongo (Geotrichum sp.) y un control (agua destilada estéril). Los resultados mostraron rápida incidencia de la enfermedad, que fue evidente a los 36 días después de la siembra (dds) y alcanzó 100% de afectacióna los 52 dds sin diferencias entre tratamientos. La severidadfue estadísticamente diferente únicamente en el tratamientode “purín de flores”, que se diferenció significativamente deltratamiento control entre los 56 y 70 dds (27,8% menos) lo que fortalece resultados de ensayos anteriores sobre su potencial como posible producto biocontrolador de la Gota. Resultados menores de severidad frente al control (no significativos) también se obtuvieron con los tratamientos “purín mezcla” y “mezcla de actinomicetos”, entre los 60 y 87 dds / In an organic farm of Tenjo (Cundinamarca, Colombia)the effect of various materials prepared from chipaca (Bidenspilosa) and microbial isolates, on the incidence and severity ofP. infestans in potato (Solanum phureja) were studied usinga completely randomized design (CRD) with subsampling andeight treatments: flowers slurry, mixed slurry; actinomyceteAC 12 (Streptomyces sp.), mix actinomycetes; isolationbacterial (Bacillus subtilis ); mixed bacteria (Bacillus subtilisand Burkholderia cepacia); fungus (Geotrichum sp.) and acontrol (sterile distilled water). The results showed rapid disease incidence, which was evident at 36 days after planting (dap) and reached 100% involvement at 52 dap, with no differences between treatments. The severity was statistically different only in the treatment of “purin of flowers”, which differed significantly from the control treatment between 56 and 70 dap (27.8,% less) which strengthens results of previous tests as a potential biocontrol product. Lower percentages of severity versus control (not significant) were also obtained with the treatments “slurry mixture” and “mixed actinomycetes”, between 60 and 87 dap

Environmental Sensor Placement with Convolutional Gaussian Neural Processes

Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to maximise measurement informativeness and place sensors efficiently, particularly in remote regions like Antarctica. Probabilistic machine learning models can evaluate placement informativeness by predicting the uncertainty reduction provided by a new sensor. Gaussian process (GP) models are widely used for this purpose, but they struggle with capturing complex non-stationary behaviour and scaling to large datasets. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues. A ConvGNP uses neural networks to parameterise a joint Gaussian distribution at arbitrary target locations, enabling flexibility and scalability. Using simulated surface air temperature anomaly over Antarctica as ground truth, the ConvGNP learns spatial and seasonal non-stationarities, outperforming a non-stationary GP baseline. In a simulated sensor placement experiment, the ConvGNP better predicts the performance boost obtained from new observations than GP baselines, leading to more informative sensor placements. We contrast our approach with physics-based sensor placement methods and propose future work towards an operational sensor placement recommendation system. This system could help to realise environmental digital twins that actively direct measurement sampling to improve the digital representation of reality.Comment: In review for the Climate Informatics 2023 special issue of Environmental Data Scienc

Environmental sensor placement with convolutional Gaussian neural processes

Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. Gaussian process (GP) models are widely used for this purpose, but they struggle with capturing complex non-stationary behaviour and scaling to large datasets. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues. A ConvGNP uses neural networks to parameterise a joint Gaussian distribution at arbitrary target locations, enabling flexibility and scalability. Using simulated surface air temperature anomaly over Antarctica as training data, the ConvGNP learns spatial and seasonal non-stationarities, outperforming a non-stationary GP baseline. In a simulated sensor placement experiment, the ConvGNP better predicts the performance boost obtained from new observations than GP baselines, leading to more informative sensor placements. We contrast our approach with physics-based sensor placement methods and propose future steps towards an operational sensor placement recommendation system. Our work could help to realise environmental digital twins that actively direct measurement sampling to improve the digital representation of reality

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

Mapping deforestation stages and spatial patterns in the Amazon rainforest using fractal analysis and data mining techniques = Mapeo de las etapas y patrones de la deforestación en el bosque Amazónico usando análisis fractales y mineria de datos

Several analyses can be derived from the land-change information provided by remote sensing image datasets. This study was pioneer to examine the stages and spatial patterns of forest loss across the Amazon rainforest according to the information provided by two methodological frameworks (fractal analysis and data mining). These frameworks were tested for analysing two operational deforestation dataset sources (Terra-i and Global Forest Change) at different extent of analysis (four increasing fixed grids sizes). As a result, both frameworks led to different conclusions regarding the dynamics of deforestation in the study area. In the fractal analysis framework, the Global Forest Change (GFC) dataset showed a higher proportion of advanced stages (fractal dimension ranging between 1.00 to 1.64) of deforestation than the Terra-i dataset. Regarding the data mining framework, it suggested the feasibility of artificial neural networks for mapping spatial patterns. The use of this algorithm with an extent of analysis of 30720 m provided the best true model performance using either the GFC or Terra-i datasets (kappa values of 0.73 and 0.70, respectively). In overall, both frameworks indicated some parts of the Amazon rainforest recent deforestation has started to compact as clearings start to agglomerate in medium and large shapes. In terms of agents of forest change, the most dominant spatial pattern typologies point to spontaneous and small agricultural colonisation as the main drivers of recent forest-change dynamics in the study area. Resumen: Una variedad de información sobre el cambio del uso del suelo y la tierra puede ser derivada de productos generados por imágenes satelitales. Este estudio fue pionero en examinar las etapas y los patrones espaciales de la pérdida de bosques en la selva Amazónica de acuerdo con la información proporcionada por dos marcos metodológicos (análisis fractal y minería de datos). Estos marcos fueron probados para analizar dos fuentes de datos de deforestación (Terra-i y Global Forest Change) en diferentes extensiones geográficas de análisis (cuatro tamaños de cuadrículas fijas crecientes). Como resultado, ambos marcos llevaron a conclusiones diferentes con respecto a la dinámica de la deforestación en el área de estudio. En el enfoque con análisis fractal, el conjunto de datos de Cambio Forestal Global (GFC) mostró una mayor proporción de etapas avanzadas (dimensión fractal que oscila entre 1.00 a 1.64) de deforestación que el conjunto de datos Terra-i. Con respecto al análisis con minería de datos, esté sugirió la viabilidad de redes neuronales artificiales para mapear patrones espaciales. El uso de este algoritmo con un análisis de extensión geográfica de 30.720 m proporcionó el mejor resultado utilizando cualquiera de los conjuntos de datos, GFC o Terra-i (valores kappa de 0,73 y 0,70, respectivamente). En general, ambos enfoques indicaron que algunas partes de la selva tropical Amazónica han comenzado a compactarse recientemente a medida que las talas comienzan a aglomerarse en formas medianas y grandes. En términos de agentes del cambio, las tipologías de patrones espaciales más dominantes apuntan a la colonización agrícola espontánea y pequeña como los principales impulsores de la dinámica reciente del cambio del bosque en el área de estudio

FAIR Research Objects for realising Open Science with the EOSC project RELIANCE

The numerous benefits of Open Science (OS) and of the four FAIR foundational principles - Findable, Accessible, Interoperable and Reusable - are increasingly valued in academia, although what OS and FAIR entail is still largely misunderstood. In such conditions, putting into practice OS and applying the FAIR principles is challenging and underrated. However, realising OS is perfectly within our grasp provided that an infrastructure supporting the management of the research lifecycle is available. ROHub (https://www.rohub.org/) is a Research Object (RO) management platform implementing three complementary technologies: Research Objects, Data Cubes and Text Mining services. ROHub enables researchers to collaboratively manage, share and preserve their research while they are still working on it (rather than after the work is finished). In this paper, three communities from Earth Sciences, namely Geohazards, Sea Monitoring and Climate Change, demonstrate how ROHub helped them to understand each other and to work openly and, more importantly, how communities of practice play an important role in facilitating reuse and interdisciplinary collaboration. These findings are illustrated with several use cases from these various communities