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Overview This dataset includes the images (visible bands for Landsat-8 or NICFI PlanetScope), auxiliary data (infrared, NCEP, forest gain, OpenStreetMap, SRTM, GFW), and data about forest loss (Global Forest Change) used to train, validate and test a model to classify direct deforestation drivers in Cameroon. Description of the files 'my_examples_landsat_final_detailed.zip': Landsat-8 images, auxiliary data and forest loss data used to train, validate and test a model for a detailed classification of deforestation drivers in Cameroon (15 classes: ‘Oil palm plantation’, ‘Timber plantation’, ‘Fruit plantation (e.g. banana)’, ‘Rubber plantation’, ‘Other large-scale plantation (e.g. tea, sugarcane)’, ‘Grassland/Shrubland’, ‘Small-scale oil palm plantation’, ‘Small-scale maize plantation’, ‘Other small-scale agriculture’, ‘Mining’, ‘Selective logging’, ‘Infrastructure’, ‘Wildfire’, ‘Hunting’, ‘Other’) 'my_examples_planet_final_detailed.zip': NICFI PlanetScope images, auxiliary data and forest loss data used to train, validate and test a model for a detailed classification of deforestation drivers in Cameroon (15 classes) 'my_examples_landsat_final.zip': Landsat-8 images, auxiliary data and forest loss data used to train, validate and test a model for a classification of deforestation drivers by groups in Cameroon (4 classes: 'Plantation', 'Grassland/Shrubland', 'Smallholder agriculture', 'Other') 'my_examples_planet_final.zip': NICFI PlanetScope images, auxiliary data and forest loss data used to train, validate and test a model for a classification of deforestation drivers by groups in Cameroon (4 classes) 'my_examples_landsat_detailed_timeseries.zip': Landsat-8 images, auxiliary data and forest loss data used to test a model for a detailed classification of deforestation drivers in Cameroon (15 classes) using multiple images and a time series analysis 'my_examples_planet_detailed_timeseries.zip': NICFI PlanetScope images, auxiliary data and forest loss data used to test a model for a detailed classification of deforestation drivers in Cameroon (15 classes) using multiple images and a time series analysis ‘labels.zip’: in csv files, the labels for each image in each folder described above (image identified by folder and coordinates or ‘path’) and matches the format of the csv files used as inputs to train, validate and test our classification model For ‘labels.zip’, we have subfolders for Landsat and PlanetScope. Then, for each type of imagery, we have subfolders for ‘detailed’, ‘groups’ and ‘time series’ which correspond to the different ‘my_examples’ folders listed above. For each folder, subfolders named with the coordinates of the centre of the images contain each:• A folder ‘images’, with a sub-folder ‘visible’ containing the PNG RGB image; and a sub-folder ‘infrared’ containing the infrared bands in a NPY file.• A folder ‘auxiliary’ with topographic and forest gain information in a NPY format, OpenStreetMap and peat data in a JSON format, and a sub-folder ‘ncep’ containing all data from NCEP in a NPY format.• The forest loss pickle file delimiting the area of forest loss. Details about the images For Landsat-8 data (courtesy of the U.S. Geological Survey), this dataset contains 332x 332 pixels RGB calibrated top-of-atmosphere (TOA) reflectance images pan-sharpened to a 15 m resolution (less than 20% cloud cover) For NICFI PlanetScope data (catalog owner: Planet), this dataset contains 332x 332 pixels monthly RGB composite with a 4.77 m resolution Details about the auxiliary data Forest gain from GFC: 30-m resolution, yearly data for 2000-2021, downloaded via Google Earth Engine Near infrared, shortwave infrared 1 and 2 bands from Landsat-8 TOA: 30-m resolution, data every 16 days for 2013-2023, downloaded via Google Earth Engine and selected using the same process as for Landsat-8 RGB images From NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products: surface level albedo and volumetric soil moisture content (depths: 0.1 m, 0.4 m, 1.0 m, 2.0m) in 0.01%; radiative fluxes (clear-sky longwave flux downward and upward, clear-sky solar flux downward and upward, direct evaporation from bare soil, longwave and shortwave radiation flux downward and upward, latent, ground and sensible heat net flux), potential evaporation rate, and sublimation in W/m²; humidity (specific, maximum specific, minimum specific) in 10-4 kg/kg; ground level precipitation in 0.1 mm; air pressure at surface level in 10 Pa; wind level (u and v component) in 0.01 m/s, water runoff at surface level in 232.01 kg/ m²; temperature in K: 22264-m resolution, available four times a day for 2011-2023, downloaded directly from the NOAA website and selected the mean of the monthly mean over 5 years before the forest loss event, the monthly maximum over 5 years before the forest loss event, and the monthly minimum over 5 years before the forest loss event for each parameter Closest street and closest city from OpenStreetMap in km: directly downloaded with the Nominatim API Altitude in m, slope and aspect in 0.01° from Shuttle Radar Topography Mission (SRTM): 30-m resolution, measured for 2000, downloaded via Google Earth Engine Presence of peat from GFW: 232-m resolution, measured for 2017, directly downloaded on the GFW website Details about Global Forest Change For each image, there is a corresponding 'forest_loss_region' .pkl file delimiting a forest loss region polygon from Global Forest Change (GFC). GFC consists of annual maps of forest cover loss with a 30-m resolution. License The NICFI PlanetScope images fall under the same license as the NICFI data program license agreement (data in 'my_examples_planet_final.zip', 'my_examples_planet_final_detailed.zip', 'my_examples_planet_detailed_timeseries.zip': subfolders '[coordinates]'>'images'>'visible'). OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF) (data in all 'my_examples' folders: subfolders '[coordinates]'>'auxiliary'>'closest_city.json'/'closest_street.json'). The documentation is licensed under the Creative Commons Attribution-ShareAlike 2.0 license (CC BY-SA 2.0). The rest of the data is under a Creative Commons Attribution 4.0 International License. The data has been transformed following the code that can be found via this link: https://github.com/aedebus/Cam-ForestNet (in 'prepare_files'). For more details about how this dataset has been created and can be used, please refer to our paper and code: https://github.com/aedebus/Cam-ForestNet. The paper can be found here: https://www.nature.com/articles/s41597-024-03384-z. Citation: Debus, A. et al. A labelled dataset to classify direct deforestation drivers from Earth Observation imagery in Cameroon. Sci Data 11, 564 (2024).

Project: A European Aerosol Research Lidar Network to Establish an Aerosol Climatology - Aerosols affect life on earth in several ways. They play an important role in the climate system; the effect of aerosols on the global climate system is one of the major uncertainties of present climate predictions. They play a major role in atmospheric chemistry and hence affect the concentrations of other potentially harmful atmospheric constituents, e.g. ozone. They are an important controlling factor for the radiation budget, in particular in the UV-B part of the spectrum. At ground level, they can be harmful, even toxic, to man, animals, and plants. Because of these adverse effects that aerosols can have on human life, it is necessary to achieve an advanced understanding of the processes that generate, redistribute, and remove aerosols in the atmosphere. A quantitative dataset describing the aerosol vertical, horizontal, and temporal distribution, including its variability on a continental scale, is necessary. The dataset is used to validate and improve models that predict the future state of the atmosphere and its dependence on different scenarios describing economic development, including those actions taken to preserve the quality of the environment. The EARLINET data set is the most comprehensive compilation of data available for this purpose. This project description is taken from: http://www.earlinet.org/index.php?id=earlinet_homepage Summary: This collection contains all measurements that have been performed in the frame of the EARLINET project during the period April 2000 - December 2015. Some of these measurements are also part of the collections 'Calipso', 'Climatology', 'SaharanDust' or 'VolcanicEruption'. In addition this collection also contains measurements from the categories 'Cirrus', 'DiurnalCycles', 'ForestFires', 'Photosmog', 'RuralUrban', and 'Stratosphere'. This collection also contains measurements not devoted to any of the above categories. More information about these categories and the contributing stations can be found in the file 'EARLINET_general_introduction.pdf' accompanying this dataset.

Project: A European Aerosol Research Lidar Network to Establish an Aerosol Climatology - Aerosols affect life on earth in several ways. They play an important role in the climate system; the effect of aerosols on the global climate system is one of the major uncertainties of present climate predictions. They play a major role in atmospheric chemistry and hence affect the concentrations of other potentially harmful atmospheric constituents, e.g. ozone. They are an important controlling factor for the radiation budget, in particular in the UV-B part of the spectrum. At ground level, they can be harmful, even toxic, to man, animals, and plants. Because of these adverse effects that aerosols can have on human life, it is necessary to achieve an advanced understanding of the processes that generate, redistribute, and remove aerosols in the atmosphere. A quantitative dataset describing the aerosol vertical, horizontal, and temporal distribution, including its variability on a continental scale, is necessary. The dataset is used to validate and improve models that predict the future state of the atmosphere and its dependence on different scenarios describing economic development, including those actions taken to preserve the quality of the environment. The EARLINET data set is the most comprehensive compilation of data available for this purpose. This project description is taken from: http://www.earlinet.org/index.php?id=earlinet_homepage Summary: The geographical distribution of the EARLINET stations is particularlysuitable for dust observation, with stations located all around the Mediterranean(from the Iberian Peninsula in the West to the Greece and Bulgaria and Romania in the East) and in the center of the Mediterranean (Italian stations) where dust intrusions are frequent, and with several stations in the central Europe where dust penetrates occasionally. A suitable observing methodology has been established within the network, based on Saharan dust alerts distributed to all EARLINET stations. The dust alert is based on the operational outputs (aerosol dust load) of the SDS-WAS (Sand and Dust Storm- Warning and Advisory System of WMO), and the Skiron models. The alerts are diffused 24 to 36 hours prior to the arrival of dust aerosols over the EARLINET sites. Runs of measurements longer than 3-hour observations, typical for the EARLINET climatological measurements are performed at the EARLINET stations in order to investigate the temporal evolution of the dust events. All aerosol backscatter and extinction profiles related to observations of Saharan dust layers are collected in the "Saharan dust" category of the EARLINET database.

Il corso online "IA e Robotica: nuove generazioni, nuove visioni" è una delle iniziative che fanno parte del progetto STACY (Secondary TeAcher CommuniTY), coordinato dal Consiglio Nazionale delle Ricerche - Istituto per le Tecnologie Didattiche (CNR-ITD). STACY è uno degli otto progetti del Training Program di RAISE, un'iniziativa finanziata dal Ministero dell'Università e della Ricerca (MUR) per la creazione e il rafforzamento di ecosistemi dell'innovazione tecnologica e digitale, e per la promozione della collaborazione tra il sistema della ricerca, il sistema produttivo e le istituzioni territoriali. La finalità principale del corso è fornire ai docenti metodi e strumenti per stimolare in aula una riflessione critica sui temi dell'Intelligenza Artificiale e della Robotica in visione prospettica, così da permettere agli studenti e alle studentesse di approcciarsi alle nuove tecnologie in maniera consapevole. Qui di seguito gli obiettivi specifici dell'azione formativa suddivisi nei 5 moduli in cui è articolato il corso online. M1 - Introduzione a AI, Robotica e Pensiero Anticipatorio - Acquisire conoscenze di base sull'Intelligenza artificiale (AI) e la Robotica - Conoscere e comprendere le possibili applicazioni dell'AI e della Robotica alla didattica - Acquisire conoscenze di base sui Future Studies e sul concetto di Pensiero Anticipatorio (Anticipatory Thinking) M2 - Perché è importante parlare di futuro - Comprendere le motivazioni per cui si parla di futuro e si promuovono i Future Studies - Riflettere sull'impatto dei Future Studies sull'individuo e sulla società - Conoscere le possibili implicazioni fattuali ed etiche dei Future Studies in vari campi di applicazione M3 - Il Metodo Backcasting - Acquisire competenze di base sul metodo del Backcasting - Comprendere le possibili applicazioni didattiche del metodo del Backcasting M4 - Progettare attività di riflessione prospettica in classe - Progettare una breve attività didattica in grado di stimolare una riflessione critica e prospettica da parte degli studenti sulle applicazioni e le possibili evoluzioni dell'AI&R - Personalizzare la progettazione didattica in base alle esigenze del gruppo classe. M5 - Realizzare in classe l'attività progettata - Condurre e sperimentare in classe l'attività progettata nel modulo precedente; - Valutare le ricadute sugli studenti e sulle studentesse dell'attività condotta in classe.

Project: A European Aerosol Research Lidar Network to Establish an Aerosol Climatology - Aerosols affect life on earth in several ways. They play an important role in the climate system; the effect of aerosols on the global climate system is one of the major uncertainties of present climate predictions. They play a major role in atmospheric chemistry and hence affect the concentrations of other potentially harmful atmospheric constituents, e.g. ozone. They are an important controlling factor for the radiation budget, in particular in the UV-B part of the spectrum. At ground level, they can be harmful, even toxic, to man, animals, and plants. Because of these adverse effects that aerosols can have on human life, it is necessary to achieve an advanced understanding of the processes that generate, redistribute, and remove aerosols in the atmosphere. A quantitative dataset describing the aerosol vertical, horizontal, and temporal distribution, including its variability on a continental scale, is necessary. The dataset is used to validate and improve models that predict the future state of the atmosphere and its dependence on different scenarios describing economic development, including those actions taken to preserve the quality of the environment. The EARLINET data set is the most comprehensive compilation of data available for this purpose. This project description is taken from: http://www.earlinet.org/index.php?id=earlinet_homepage Summary: Since the beginning of CALIPSO observations in June 2006 EARLINET has performed correlative measurements during nearby overpasses of the satellite at individual stations following a dedicated observational strategy. The EARLINET-CALIPSO correlative measurement plan considers the criteria established in the CALIPSO validation plan (http://calipsovalidation.hamptonu.edu). Participating EARLINET stations perform measurements, as close in time as possible and for a period of at least 30 min up to several hours, when CALIPSO overpasses their location within a horizontal radius of 100 km. Within the 16-day observational cycle of CALIPSO each station is overpassed within this distance 1-2 times during daytime (typically between 1100 and 1400 UTC) and 1-2 times during night time (typically between 0000 and 0300 UTC). Additional measurements are performed, mainly on a non-regular basis, when CALIPSO overpasses a neighboring station in order to study the horizontal variability of the aerosol distribution. The time schedule for correlative observations is calculated starting from the high-resolution ground-track data provided by NASA, and is updated and distributed to whole network weekly. The EARLINET-CALIPSO correlative dataset represents a statistically significant data set to be used for the validation and full exploitation of the CALIPSO mission, for studying the representativeness of cross sections along an orbit against network observations on a continental scale, and for supporting the continuous, harmonized observation of aerosol and clouds with remote-sensing techniques from space over long time periods.

Project: A European Aerosol Research Lidar Network to Establish an Aerosol Climatology - Aerosols affect life on earth in several ways. They play an important role in the climate system; the effect of aerosols on the global climate system is one of the major uncertainties of present climate predictions. They play a major role in atmospheric chemistry and hence affect the concentrations of other potentially harmful atmospheric constituents, e.g. ozone. They are an important controlling factor for the radiation budget, in particular in the UV-B part of the spectrum. At ground level, they can be harmful, even toxic, to man, animals, and plants. Because of these adverse effects that aerosols can have on human life, it is necessary to achieve an advanced understanding of the processes that generate, redistribute, and remove aerosols in the atmosphere. A quantitative dataset describing the aerosol vertical, horizontal, and temporal distribution, including its variability on a continental scale, is necessary. The dataset is used to validate and improve models that predict the future state of the atmosphere and its dependence on different scenarios describing economic development, including those actions taken to preserve the quality of the environment. The EARLINET data set is the most comprehensive compilation of data available for this purpose. This project description is taken from: http://www.earlinet.org/index.php?id=earlinet_homepage Summary: EARLINET climatological lidar observations are performed on a regular schedule of one daytime measurement per week around noon (on Monday), when the boundary layer is usually well developed, and two night-time measurements per week (on Monday and Thursday), with low background light, in order to perform Raman extinction measurements. This regular schedule for observations minimizes the bias in the dataset possibly related to specific measurement conditions. The resulting dataset is used to obtain unbiased data for climatological studies. This dataset contains profiles of aerosol extinction, backscatter and lidar ratio. Several aerosol extinction/backscatter datasets can be present for the same climatological measurement in order to provide profiles either with a better temporal resolution or with an extended height range by using a larger temporal average. This is by far the largest ground based dataset on the aerosol vertical distribution, and it is the only one which is collected systematically and is covering a whole continent.

Project: A European Aerosol Research Lidar Network to Establish an Aerosol Climatology - Aerosols affect life on earth in several ways. They play an important role in the climate system; the effect of aerosols on the global climate system is one of the major uncertainties of present climate predictions. They play a major role in atmospheric chemistry and hence affect the concentrations of other potentially harmful atmospheric constituents, e.g. ozone. They are an important controlling factor for the radiation budget, in particular in the UV-B part of the spectrum. At ground level, they can be harmful, even toxic, to man, animals, and plants. Because of these adverse effects that aerosols can have on human life, it is necessary to achieve an advanced understanding of the processes that generate, redistribute, and remove aerosols in the atmosphere. A quantitative dataset describing the aerosol vertical, horizontal, and temporal distribution, including its variability on a continental scale, is necessary. The dataset is used to validate and improve models that predict the future state of the atmosphere and its dependence on different scenarios describing economic development, including those actions taken to preserve the quality of the environment. The EARLINET data set is the most comprehensive compilation of data available for this purpose. This project description is taken from: http://www.earlinet.org/index.php?id=earlinet_homepage Summary: Aerosols originating from volcanic emissions have an impact on the climate: sulfate and ash particles from volcanic emissions reflect solar radiation, act as cloud condensation and ice nuclei, and modify the radiative properties and lifetime of clouds, and therefore influence the precipitation cycle. These volcanic particles can also have an impact on environmental conditions and could be very dangerous for aircraft in flight. In addition to the routine measurements, further EARLINET observations are devoted to monitor volcano eruptions. The EARLINET volcanic dataset includes extended observations related to two different volcanoes in Europe Mt. Etna (2001 and 2002 eruptions), and the Eyjafjallajökull volcano in Iceland (April - May 2010 eruption). This dataset includes also events of volcanic eruptions in the North Pacific region (2008-2010) that emitted sulfuric acid droplets into the upper troposphere lower stratosphere (UTLS) height region of the northern hemisphere. The EARLINET volcanic observations in the UTLS are complemented by the long-term stratospheric aerosol observations collected in the Stratosphere category.

Current process-based vegetation models are complex scientific tools that require proper evaluation of the different processes included in the models to prove that the models can be used to integrate our understanding of forest ecosystems and project climate change impacts on forests. The PROFOUND database (PROFOUND DB) described here aims to bring together data from a wide range of data sources to evaluate vegetation models and simulate climate impacts at the forest stand scale. It has been designed to fulfill two objectives:- Allow for a thorough evaluation of complex, process-based vegetation models using multiple data streams covering a range of processes at different temporal scales- Allow for climate impact assessments by providing the latest climate scenario data. Therefore, the PROFOUND DB provides general a site description as well as soil, climate, CO2, Nitrogen deposition, tree-level, forest stand-level and remote sensing data for 9 forest stands spread throughout Europe. Moreover, for a subset of 5 sites, also time series of carbon fluxes, energy balances and soil water are available. The climate and nitrogen deposition data contains several datasets for the historic period and a wide range of future climate change scenarios following the Representative Emission Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). In addition, we also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND Database is available freely but we incite users to respect the data policies of the individual datasets as provided in the metadata of each data file. The database can also be accessed via the PROFOUND R-package, which provides basic functions to explore, plot and extract the data. The data (PROFOUND DB) are provided in two different versions (ProfoundData.sqlite download as ProfoundData.zip, ProfoundData_ASCII.zip) and documented by the following three documents: (1) PROFOUNDdatabase.pdf: describes the structure, organisation and content of the PROFOUND DB.(2) PROFOUNDsites.pdf: displays the main data of the PROFOUND DB for each of the 9 forest sites in tables and plots.(3) ProfoundData.pdf: explains how to use the PROFOUND R-Package "ProfoundData" to access the PROFOUND DB and provides example scripts on how to apply it.