Failures in urban areas’ solid waste management lead to clandestine garbage dumping and pollution. This affects sanitation and public human hygiene, deteriorates quality of life, and contributes to deprivation. This study aimed to test a combination of machine learning, high-resolution earth observation and GIS data to detect diverse categories of residual waste on the streets, such as sacks and construction debris. We conceptualised five different classes of solid waste from image interpretation: “Sure”, “Half-sure”, “Not-sure”, “Dispersed”, and “Non-garbage”. We tested a combination of k-means-based segmentation and supervised random forest to investigate the capabilities of automatic classification of these waste classes. The model can detect the presence of solid waste on the streets and achieved an accuracy of up from 73.95%–95.76% for the class “Sure”. Moreover, a building extraction using an EfficientNet deep-learning-based semantic segmentation allowed masking the rooftops. This improved the accuracy of the classes “Sure” and “Non-garbage”. The systematic evaluation of all parameters considered in this model provides a robust and reliable method of solid waste detection for decision-makers. These results highlight areas where insufficient waste management affects the citizens of a given city.

As remote sensing (RS) applications and resources continue to expand, their importance for ecology and conservation increases – and so does the need for effective and successful training of professionals working in those fields. Methodological and applied courses often form part of university curricula, but their practical and long‐term benefits only become clear afterwards. Having recently received such training in an interdisciplinary master's programme, we provide our perspectives on our shared education. Through an online survey we include experiences of students and professionals in different fields. Most participants perceive their RS education as useful for their career, but express a need for more training at university level. Hands‐on projects are considered the most effective learning method. Besides methodological knowledge, soft skills are clear gains, including problem solving, self‐learning and finding individual solutions, and the ability to work in interdisciplinary teams. The largest identified gaps in current RS training concern the application regarding policy making, methodology and conservation. To successfully prepare students for a career, study programmes need to provide RS courses based on state‐of‐the‐art methods, including programming, and interdisciplinary projects linking research and practice supported by a sound technical background.

The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600%, a severe loss of cropland (−24%), a moderate reduction in meadows (−4%), and a small gain in forests (+4%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65% for historical maps of poor quality and the maximum was 91% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services.

La construcción de una agenda de Adaptación al cambio climático es un proceso políticamente complejo, ni neutral, ni intrínsecamente benigno que crea un campo discursivo para la acción de distintas entidades sociales, con intereses y perspectivas particulares que proponen direcciones de transformación con efectos sociales concretos. Cabe preguntarse entonces, cuáles son esas direcciones, desde dónde se hacen y a qué posición política tributan. Para Venezuela, nación considerada como Vulnerable por Naciones Unidas (CMNUCC) ¿qué caracteriza al discurso tecnopolítico con el que contribuye su comunidad académica a esa agenda? y ¿cómo representa el proceso de Adaptación en él?. En este trabajo se respondieron estas preguntas analizando la estructura de las redes de colaboración y capital social asociado a los autores y grupos de investigación identificados en la literatura, así como sus prácticas científicas, descritas a través de la sistematización de información relativa a los enfoques metodológicos, productividad científica entre otros. Se encuentra una comunidad altamente fragmentada inscrita en la narrativa de gestión de riesgos y contención de impactos del cambio climático, característica del momento histórico de construcción de consensos multilaterales y de la noción de cambio climático antropogénico. Se discuten algunas de implicaciones ecológico-políticas de los resultados obtenidos.