Automatic classification of land cover from LUCAS in-situ landscape photos using semantic segmentation and a Random Forest model

Spatially explicit information on land cover (LC) is commonly derived using remote sensing, but the lack of training data still remains a major challenge for producing accurate LC products. Here, we develop a computer vision methodology to extract LC information from photos from the Land Use-Land Cover Area Frame Survey (LUCAS). Given the large number of photographs available and the comprehensive spatial coverage, the objective is to show how the automatic classification of photos could be used to develop reference data sets for training and validation of LC products as well as other purposes. We first selected a representative sample of 1120 photos covering eight major LC types across the European Union. We then applied semantic segmentation to these photos using a neural network (Deeplabv3+) trained with the ADE20k dataset. For each photo, we extracted the original LC identified by the LUCAS surveyor, the segmented objects, and the pixel count for each ADE20k class. Using the latter as input features, we then trained a Random Forest model to classify the LC of the photo. Examining the relationship between the objects/features extracted by Deeplabv3+ and the LC labels provided by the LUCAS surveyors demonstrated how the LC classes can be decomposed into multiple objects, highlighting the complexity of LC classification from photographs. The results of the classification show a mean F1 Score of 89%, increasing to 93% when the Wetland class is not considered. Based on these results, this approach holds promise for the automated retrieval of LC information from the rich source of LUCAS photographs as well as the increasing number of geo-referenced photos now becoming available through social media and sites like Mapillary or Google Street View

Deviating vital signs in continuous monitoring prior to discharge and risk of readmission:an observational study

Premature discharge may result in readmission while longer hospitalization may increase risk of complications such as immobilization and reduce hospital capacity. Continuous monitoring detects more deviating vital signs than intermittent measurements and may help identify patients at risk of deterioration after discharge. We aimed to investigate the association between deviating vital signs detected by continuous monitoring prior to discharge and risk of readmission within 30 days. Patients undergoing elective major abdominal surgery or admitted with acute exacerbation of chronic obstructive pulmonary disease were included in this study. Eligible patients had vital signs monitored continuously within the last 24 h prior to discharge. The association between sustained deviated vital signs and readmission risk was analyzed by using Mann–Whitney’s U test and Chi-square test. A total of 51 out of 265 patients (19%) were readmitted within 30 days. Deviated respiratory vital signs occurred frequently in both groups: desaturation &lt; 88% for at least ten minutes was seen in 66% of patients who were readmitted and in 62% of those who were not (p = 0.62) while desaturation &lt; 85% for at least five minutes was seen in 58% of readmitted and 52% of non-readmitted patients (p = 0.5). At least one sustained deviated vital sign was detected in 90% and 85% of readmitted patients and non-readmitted patients, respectively (p = 0.2). Deviating vital signs prior to hospital discharge were frequent but not associated with increased risk of readmission within 30 days. Further exploration of deviating vital signs using continuous monitoring is needed.</p

Continuously monitored vital signs for detection of myocardial injury in high-risk patients - An observational study

BACKGROUND: Patients are at risk of myocardial injury after major non‐cardiac surgery and during acute illness. Myocardial injury is associated with mortality, but often asymptomatic and currently detected through intermittent cardiac biomarker screening. This delays diagnosis, where vital signs deviations may serve as a proxy for early signs of myocardial injury. This study aimed to assess the association between continuous monitored vital sign deviations and subsequent myocardial injury following major abdominal cancer surgery and during acute exacerbation of chronic obstructive pulmonary disease. METHODS: Patients undergoing major abdominal cancer surgery or admitted with acute exacerbation of chronic obstructive pulmonary disease had daily troponin measurements. Continuous wireless monitoring of several vital signs was performed for up to 96 h after admission or surgery. The primary exposure was cumulative duration of peripheral oxygen saturation (SpO(2)) below 85% in the 24 h before the primary outcome of myocardial injury, defined as a new onset ischaemic troponin elevation assessed daily. If no myocardial injury occurred, the primary exposure was based on the first 24 h of measurement. RESULTS: A total of 662 patients were continuously monitored and 113 (17%) had a myocardial injury. Cumulative duration of SpO(2)  110 bpm and HR > 130 bpm) and tachypnoea (RR > 24 min(−1) and RR > 30 min(−1)) were also significantly associated with myocardial injury (p < .04, for all). CONCLUSION: Duration of severely low SpO(2) detected by continuous wireless monitoring is significantly associated with myocardial injury in high‐risk patients admitted to hospital wards. The effect of early detection and interventions should be assessed next