The competitiveness and innovation of the EU space sector depend on high educational standards and the availability of skilled professionals in the field, as well as the possibility for these professionals to enhance and update their skills through their careers to adapt to changing circumstances. This paper summarizes the collection and analysis of different educational programs in the space sector across the EU-27+UK. We identified and analyzed 3591 courses offered by 132 Degree Programs (DPs) at Bachelor (25 DPs) and Master (107 DPs) levels, 19 PhD programs, and 60 continuous education courses. The identified programs, learning objectives, and course descriptions have been mapped across different segments of the value chain of space activities (upstream, midstream, and downstream) and space-relevant knowledge domains and knowledge areas taxonomy. We created a structured and curated online catalog, that allows users to search, and retrieve the education offers in the space-related sectors.

Selective logging represents a primary driver of forest degradation, being early germs of the deforestation process. It negatively impacts the remaining forests, leading to biodiversity losses, and catalyzing in the long term the climate changes. It is a process related to spatial-temporal changes in the forest areas, and consequently can be monitored by means of satellite images. In this work, we evaluated the potential of image time series of both Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data to detect selective logging using several variations of the Long ShortTerm Memory (LSTM) network. Particularly, we exploited complex-valued SAR images that contain both intensity and phase information to capture small changes caused by selective logging. The experiments using optical data achieved the highest accuracy of 98.21%, while those using SAR data reached a maximum accuracy of 72.68%. The results demonstrated the effectiveness of optical images and the potential of complex-valued SAR data for selective logging detection.

The rapid urbanization in many regions worldwide results in the proliferation of deprived urban areas, also known as slums or informal settlements. Our study addresses the pressing need for accurate information by investigating User and Data-centric Artificial Intelligence (AI)-based methods for mapping deprived urban areas and extracting information supporting the Sustainable Development Goals (SDG) Indicator 11.1.1. In collaboration with local communities and several (inter)national stakehlders, we co-designed AI strategies based on free or low-cost Earth Observation (EO) and geospatial data to map informal settlements in eight cties across the globe. The AI methods design, data collection, and validation strategies follow an iterative and agile process consisting of progressive refinement stages necessary to collect reliable labeled data and take user requirements into their centre. Our findings indicate that the combination of Sentinel-2 and morphometric features yields the most accurate results.

Despite its environmental and societal importance, accurately mapping the Brazilian Cerrado’s vegetation is still an open challenge. Its diverse but spectrally similar physiognomies are difficult to be identified and mapped by state-of-the-art methods from only medium-to high-resolution optical images. This work investigates the fusion of Synthetic Aperture Radar (SAR) and optical data in convolutional neural network architectures to map the Cerrado according to a 2-level class hierarchy. Additionally, the proposed model is designed to deal with uncertainties that are brought by the difference in resolution between the input images (at 10m) and the reference data (at 30m). We tested four data fusion strategies and showed that the position for the data combination is important for the network to learn better features.

Cloud and cloud shadows are a main source of concern when using dense time series of optical remote sensing images. Machine learning has the potential to effortlessly overcome this barrier using Long Short-Term Memory (LSTM), which is a deep learning algorithm created to analyze time series and has parts dedicated to suppress irrelevant information. In this context, we evaluated the ability of models with LSTM layers to create LULC maps using either cloudy or gap-filled Landsat-8/OLI time series for Pantanal. Five different LSTM models were trained with tenfold cross validation using samples gathered by the authors. Our results indicate that simple models are more accurate with filled time series, but this difference in accuracy was not present in more complex models. We also present a LULC map created for the entire Pantanal.

Hypersaline tidal flats are plane areas usually related to mangrove forests, acting as guard and buffer against rising sea levels, and as maintainer of regional biodiversity. Such areas are primarily impacted by anthropogenic and natural activities, such as sea-salt extraction and pollution, so identifying and monitoring them is an important and challenging task. The present work uses a U-shaped Convolutional Neural Network architecture to systematically classify such formations over Landsat imagery. A large dataset containing data from 1985 to 2021 of the Brazilian Coastal Zone is used to train and evaluate our model. Experimental results show that the total area increased by 58.6 km2from 1985 to 2001, and decreased by approximately 92 km2from 2001 to 2021, representing a total reduction of ≈ 33.34 km2for the entire period. We also show that our model outperforms a related solution trained with the same dataset, achieving 70% and 86% for 1985 and 2020 respectively, against 69% and 82%.

We consider the problem of cost effective active learning for semantic segmentation, which aims at reducing the efforts of semantically annotating images. Current studies have ignored the inclusion of cost of labeling into their active learning frameworks. To this end, we first present a novel cost prediction module based on what we call the M-Net. M-Net combines the power of unsupervised W-Net and supervised U-Net to compute a refined segmentation map. The refined segmentation map is used to estimate the cost of annotations. The cost of annotation is estimated by the number of clicks required to annotate an image. To solve this task, we make use of the harris corner detector algorithm to estimate the location of the clicks required to annotate an image. Finally, we employ a multi armed bandit setting to minimize the cost of annotations while maximizing the performance of the semantic segmentation task. The M-Net outperforms fully supervised U-Net with +4.37 Acc and +3.75 mIoU. The proposed active learning framework also outperforms the existing baselines to prove the relevance of the approach in the current paradigm.

The Brazilian Palm Swamps (Veredas) are a vegetation physiognomy of the Cerrado biome. It has a critical importance for biodiversity and also for groundwater sources conservation. With the irrigated agriculture intensification, it's been significantly impacted. Mapping this physiognomy is important to delimit this vegetation type to provide subsides for public policy and monitoring programs. Pixel-based methods do not succeed, since the spatial context is important for this physiognomy. Object-based methods are a great potential on this sense. Deep Learning methods, particularly the convolutional neural networks (CNN), are increasing considerably as a solution for these challenges. We applied both methods in two regions of the Cerrado and evaluated the model transferability. The results are promising, with training model overall accuracies higher than 90% for both methods. The CNN performed better when transferred a different region. We discussed some advantages and limitations, and pointed out to improvements that can still be done.

Automating the systematic monitoring of deforestation in the Brazilian biomes has become imperative. In this sense, a promising research field lies upon the exploitation of orbital imaging based on Synthetic Aperture Radar (SAR) sensors, since this technology is less affected by cloud cover, allowing systematic data acquisitions. In addition, the growing availability of with no charge SAR data products enables investigations on the use of time series extracted from this category of instruments, paving the way for more sophisticated temporal analyzes. This work presents the results of a SAR time series classification model designed to identify clearcut deforestation patterns in time, through an Artificial Intelligence approach known as Recurrent Neural Networks. The classification was performed using 5216 samples of Sentinel-1 time series within the Amazon basin, reaching an overall accuracy of 96.74%.

We address the task of mapping deforested areas in the Brazilian Amazon. Accurate maps are an important tool for informing effective deforestation containment policies. The main existing approaches to this task are largely manual, requiring significant effort by trained experts. To reduce this effort, we propose a fully automatic approach based on spatio-temporal deep convolutional neural networks. We introduce several domain-specific components, including approaches for: image preprocessing; handling image noise, such as clouds and shadow; and constructing the training data set. We show that our preprocessing protocol reduces the impact of noise in the training data set. Furthermore, we propose two spatio-temporal variations of the U-Net architecture, which make it possible to incorporate both spatial and temporal contexts. Using a large, real-world data set, we show that our method outperforms a traditional U-Net architecture, thus achieving approximately 95% accuracy.

The Brazilian Savannah, or Cerrado, has gained vital importance in the discussions about sustainable land development after the conversion of half of its natural vegetation. For the last two decades, most of the agricultural expansion in Brazil has occurred in this biome. This is related to technological improvements in agriculture as well as to environmental compliance policies that have effectively reduced soybean expansion in the Brazilian Amazon biome. Therefore, remotely sensed imagery, pattern recognition and image processing techniques have been employed to analyze and monitor the land dynamics over Cerrado. In this work, we present a brief review on Land Use and Land Cover mapping (LULC) in the Cerrado biome from an application perspective: natural vegetation, pastureland, agriculture, and deforestation. In this review we selected some studies whose results could contribute to the development of more detailed and accurate LULC maps for the Cerrado biome.

Mapping agriculture with high accuracy is important to generate reliable information about crop production. Pixel-based methods still present problems with noise and usually require post-processing approaches to reach satisfactory results. Object-based Image Analysis (OBIA) enable the detection of homogeneous objects in remote sensing images based on spectral similarity. However, traditional OBIA does not consider the multi-temporal characteristics of land cover or land use, such as agriculture. The objective of this study is to evaluate a phenological object-based approach with dense Landsat image time series for mapping agriculture in different level of detail in the Brazilian Cerrado. We derived pixel-wise EVI fitted time series with 8-day temporal resolution and applied multi-resolution segmentation using all image bands to incorporate the influence of space and time. Then we generated phenological metrics and applied OBIA of agricultural lands in Brazil using a hierarchical classification scheme. The overall accuracies for each hierarchical level were around 90%, and the spatial consistency of the generated maps is promising.

The Brazilian Savannah, also known as Cerrado Biome, is a hotspot for the Brazilian biodiversity and is also important for this country water supply. One of the most active Brazilian agricultural frontiers, the region has a history of primary vegetation suppression. Accurately map this phenomenon is an important step to inform and enable government conservation programs. In this work, we used a Long Short-Term Memory network to generate a deforestation map for the Cerrado. The PRODES deforestation inventory was used as ground truth during training and evaluation. We used as inputs a dense Landsat 8 time series composed by 6 spectral bands and 3 vegetation indices, as well as the SRTM terrain slope. The methodology was tested on an area comprising about 31,450 km2, achieving approximately 98.5% global accuracy.

Deep Learning (DL) methods are currently the state-of-the-art in Machine Learning and Pattern Recognition. In recent years, DL has been successfully applied to Remote Sensing (RS) image processing for several tasks, from pre-processing to classification. This paper presents DeepGeo, a toolbox that provides state-of-the-art DL algorithms for RS image classification and analysis. DeepGeo focuses on providing easy-to-use and extensible methods, making it easier to those RS analysts without strong programming skills. It is distributed as free and open source package and is available at https: //github.com/rvmaretto/deepgeo.