Accurate building height estimation is essential to support urbanization monitoring, environmental impact analysis and sustainable urban planning. However, conducting large-scale building height estimation remains a significant challenge. While deep learning (DL) has proven effective for large-scale mapping tasks, there is a lack of advanced DL models specifically tailored for height estimation, particularly when using open-source Earth observation data. In this study, we propose T-SwinUNet, an advanced DL model for large-scale building height estimation leveraging Sentinel-1 SAR and Sentinel-2 multispectral time series. T-SwinUNet model contains a feature extractor with local/global feature comprehension capabilities, a temporal attention module to learn the correlation between constant and variable features of building objects over time and an efficient multitask decoder to predict building height at 10 m spatial resolution. The model is trained and evaluated on data from the Netherlands, Switzerland, Estonia, and Germany, and its generalizability is evaluated on an out-of-distribution (OOD) test set from ten additional cities from other European countries. Our study incorporates extensive model evaluations, ablation experiments, and comparisons with established models. T-SwinUNet predicts building height with a Root Mean Square Error (RMSE) of 1.89 m, outperforming state-of-the-art models at 10 m spatial resolution. Its strong generalization to the OOD test set (RMSE of 3.2 m) underscores its potential for low-cost building height estimation across Europe, with future scalability to other regions. Furthermore, the assessment at 100 m resolution reveals that T-SwinUNet (0.29 m RMSE, 0.75 R2) also outperformed the global building height product GHSL-Built-H R2023A product(0.56 m RMSE and 0.37 R2). Our implementation is available at: https://github.com/RituYadav92/Building-Height-Estimation.

In this study, we propose a novel unsupervised Change Detection (CD) model to detect flood extent using Synthetic Aperture Radar (SAR) time series data. The proposed model is based on a spatiotemporal variational autoencoder, trained with reconstruction and contrastive learning techniques. The change maps are generated with a proposed novel algorithm that utilizes differences in latent feature distributions between pre-flood and post-flood data. The model is evaluated on nine different flood events by comparing the results with reference flood maps collected from the Copernicus Emergency Management Services (CEMS) and Sen1Floods11 dataset. We conducted a range of experiments and ablation studies to investigate the performance of our model. We compared the results with existing unsupervised models. The model achieved an average of 70% Intersection over Union (IoU) score which is at least 7% better than the IoU from existing unsupervised CD models. In the generalizability test, the proposed model outperformed supervised models ADS-Net (by 10% IoU) and DAUSAR (by 8% IoU), both trained on Sen1Floods11 and tested on CEMS sites. Our implementation will be available here https://github.com/RituYadav92/CLVAE-Unsupervised_Change_Detection_TimeSeriesSAR.

Accurate estimation of building heights is essential for urban planning, infrastructure management, and environmental analysis. In this study, we propose a supervised Multimodal Building Height Regression Network (MBHR-Net) for estimating building heights at 10m spatial resolution using Sentinel-1 (S1) and Sentinel-2 (S2) satellite time series. S1 provides Synthetic Aperture Radar (SAR) data that offers valuable information on building structures, while S2 provides multispectral data that is sensitive to different land cover types, vegetation phenology, and building shadows. Our MBHR-Net aims to extract meaningful features from the S1 and S2 images to learn complex spatio-temporal relationships between image patterns and building heights. The model is trained and tested in 10 cities in the Netherlands. Root Mean Squared Error (RMSE), Intersection over Union (IOU), and R-squared (R2) score metrics are used to evaluate the performance of the model. The preliminary results (3.73m RMSE, 0.95 IoU, 0.61 R 2 ) demonstrate the effectiveness of our deep learning model in accurately estimating building heights, showcasing its potential for urban planning, environmental impact analysis, and other related applications.

Above Ground Biomass is an important variable as forests play a crucial role in mitigating climate change as they act as an efficient, natural and cost-effective carbon sink. Traditional field and airborne LiDAR measurements have been proven to provide reliable estimations of forest biomass. Nevertheless, the use of these techniques at a large scale can be challenging and expensive. Satellite data have been widely used as a valuable tool in estimating biomass on a global scale. However, the full potential of dense multi-modal satellite time series data, in combination with modern Deep Learning (DL) approaches, has yet to be fully explored. The aim of the "BioMassters" data challenge and benchmark dataset is to investigate the potential of multi-modal satellite data (Sentinel-1 SAR and Sentinel-2 MSI) to estimate forest biomass at a large scale using the Finnish Forest Centre's open forest and nature airborne LiDAR data as a reference. The performance of the top three baseline models shows the potential of DL to produce accurate and higher-resolution biomass maps. The dataset and the code are available on the project website: https://nascetti-a.github.io/BioMasster/.

Change detection using earth observation data plays a vital role in quantifying the impact of disasters in affected areas. While data sources like Sentinel-2 provide rich optical information, they are often hindered by cloud cover, limiting their usage in disaster scenarios. However, leveraging pre-disaster optical data can offer valuable contextual information about the area such as landcover type, vegetation cover, soil types, enabling a better understanding of the disaster’s impact. In this study, we develop a model to assess the contribution of pre-disaster Sentinel-2 data in change detection tasks, focusing on disaster-affected areas. The proposed Context-Aware Change Detection Network (CACDN) utilizes a combination of pre-disaster Sentinel-2 data, pre and post-disaster Sentinel-1 data and ancillary Digital Elevation Models (DEMs) data. The model is validated on flood and landslide detection and evaluated using three metrics: Area Under the Precision-Recall Curve (AUPRC), Intersection over Union (IoU), and mean IoU. The preliminary results show significant improvement (4%, AUPRC, 3-7% IoU, 3-6% mean IoU) in model’s change detection capabilities when incorporated with pre-disaster optical data reflecting the effectiveness of using contextual information for accurate flood and landslide detection.

Human civilization has an increasingly powerful influence on the earthsystem. Affected by climate change and land-use change, floods are occurringacross the globe and are expected to increase in the coming years. Currentsituations urge more focus on efficient monitoring of floods and detecting impactedareas. Earth observations are an invaluable source for monitoring theEarth’s surface at a large scale. In particular, the Sentinel-1 Synthetic ApertureRadar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) missionsoffer high-resolution data with frequent global revisits that are widely usedfor flood detection.Current solutions such as Copernicus Emergency Management Services(CEMS), MODIS (Moderate Resolution Imaging Spectroradiometer) globalflood product, and many others use data from Sentinel and multiple othersatellites to detect floods. Although existing solutions are helpful, they alsohave several limitations. For instance, solutions like MODIS global floodproduct detect floods solely on optical images causing poor or no detection incloudy areas. In addition, these solutions are threshold-based and often requirecriteria-based adjustments. Furthermore, these solutions do not leveragerich spatial information between neighboring pixels and don’t use temporalfeatures of time series data. Therefore, advanced processing algorithms areneeded to provide a reliable method for flood detection.This thesis presents three Deep Learning (DL) models for flood detection.The first two models are supervised segmentation models proposed todetect floods on uni-temporal Sentinel-1 SAR data. The study sites containfloods from Bolivia, Ghana, India, Mekong, Nigeria, Pakistan, Paraguay,Somalia, Spain, Sri Lanka and USA. The third model is an unsupervised spatiotemporalchange detection (CD) model that detects floods on time series ofSentinel-1 SAR data. The study sites contain floods from Slovakia, Somalia,Spain, Bolivia, Mekong, Bosnia, Australia, Scotland and Germany.The two supervised segmentation models propose improving flood detectionwith the help of self-attention mechanism and fusion of Sentinel-1 SARwith more contextual information. The first network is ’Attentive U-Net’. Ittakes Sentinel-1 channels VV (vertical transmit, vertical receive), VH (verticaltransmit, horizontal receive), and the ratio VV/VH as input. The networkuses spatial and channel-wise self-attention to enhance feature maps resultingin better segmentation. The second network is a dual-stream attentive ’Fusionnetwork’, where the global low-resolution elevation data and permanent watermasks are fused with Sentinel-1 (VV, VH) data. The ’Attentive U-Net’ yields67.2% Intersection over Union (IoU), and the ’Fusion network’ gave 69.5%IoU on the Sen1Floods11 dataset. The performance gain is 3 to 5% IoUwith respect to the existing supervised models like FCNN (49.3% IoU score),U2Net (62% IoU score), and BASNet (64% IoU score). Quantitatively, thetwo proposed networks show significant improvement over benchmark methodsdemonstrating their potential. The qualitative analysis demonstrates thecontribution of low-resolution elevation and a permanent water mask in enhancingflood detection. Ablation experiments further clarify the effectiveness of ratio, self-attention, ratio and various design choices made in proposed networks.Furthermore, to improve across-region generalizability of the flood detectionmodel and to eliminate the dependency on labels, a novel unsupervisedCD model is presented that detects floods as changes on SAR time seriesdata. The proposed model is trained to learn spatiotemporal features of theSAR time series data with the help of unsupervised learning techniques, reconstruction,and contrastive learning. The change maps are generated witha novel algorithm that utilizes the learned latent feature distributions of preand post-flood data. The model achieved an average of 70% IoU score, outperformingexisting flood detection models like RaVAEn (45.03% IoU score),cGAN (51.49% IoU score) and SCCN (54.87% IoU score) with a significantminimum margin of 15% IoU score. The proposed model is tested for generalizabilityand outperformed supervised models ADS-Net and DAUSAR whentested on unseen CEMS flood sites. In addition, an automatic change monitoringand change point detection framework is proposed. The framework isbased on the proposed unsupervised CD model where time series data is processedthrough the model to identify percentage change at each time stampand the change point is detected by identifying the date on which significantchange started to reflect on SAR data. When integrated with high temporaldata i.e. daily images from ICEYE, the framework can help in continuousflood monitoring and early detection of slowly proceeding disaster events,giving more time for response.Overall, this thesis contributes supervised and unsupervised flood detectionmodels, enabling comprehensive and widely applicable flood mapping andmonitoring capabilities. These advancements facilitate near-real-time disasterresponse and resilient urban development, thus contributing to SDG 11 -Sustainable Cities and Communities.

Den mänskliga civilisationen har ett allt starkare inflytande på jordsystemet. Påverkad av klimatförändringar och förändringar i markanvändningen sker översvämningar över hela världen och förväntas öka under de kommande åren. Nuvarande situationer kräver mer fokus på effektiv övervakning av översvämningar och upptäckt av drabbade områden. Jordobservationer är en ovärderlig källa för att övervaka jordens yta i stor skala. Särskilt Sentinel- 1 Synthetic Aperture Radar (SAR) och Sentinel-2 MultiSpectral Instrument (MSI)-uppdrag erbjuder högupplösta data med frekventa globala återbesök som används ofta för att detektera översvämningar.

Aktuella lösningar som Copernicus Emergency Management Services (CEMS), MODIS (Moderate Resolution Imaging Spectroradiometer) globala översvämningsprodukter och många andra använder data från Sentinel och flera andra satelliter för att upptäcka översvämningar. Även om befintliga lösningar är användbara, har de också flera begränsningar. Till exempel upptäcker lösningar som MODIS globala översvämningsprodukt översvämningar enbart på optiska bilder som orsakar dålig eller ingen detektering i molniga områden. Dessutom är dessa lösningar tröskelbaserade och kräver ofta kriteriebaserade justeringar. Dessutom utnyttjar dessa lösningar inte rik rumslig information mellan angränsande pixlar och använder inte tidsseriedata. Därför behövs avancerade bearbetningsalgoritmer för att tillhandahålla en tillförlitlig metod för översvämningsdetektering.

Denna avhandling presenterar tre modeller för djupinlärning (DL) för översvämningsdetektering. De två första modellerna är övervakade segmenteringsmodeller som föreslagits för att upptäcka översvämningar på uni-temporala Sentinel-1 SAR-data. Studieplatserna innehåller översvämningar från Bolivia, Ghana, Indien, Mekong, Nigeria, Pakistan, Paraguay, Somalia, Spanien, Sri Lanka och USA. Den tredje modellen är en oövervakad modell för upptäckt av spatiotemporal förändring (CD) som detekterar översvämningar på tidsserier av Sentinel-1 SAR-data. Studieplatserna innehåller översvämningar från Slovakien, Somalia, Spanien, Bolivia, Mekong, Bosnien, Australien, Skottland och Tyskland.

De två övervakade segmenteringsmodellerna föreslår förbättring av översvämningsdetektering med hjälp av självuppmärksamhetsmekanism och fusion av Sentinel-1 SAR med mer kontextuell information. Det första nätverket är ’Attentive U-Net’. Den tar Sentinel-1-kanalerna VV (vertikal sändning, vertikal mottagning), VH (vertikal sändning, horisontell mottagning) och förhållandet VV/VH som ingång. Nätverket använder rumslig och kanalvis självuppmärksamhet för att förbättra funktionskartor vilket resulterar i bättre segmentering. Det andra nätverket är ett dubbelströms uppmärksamt Fusion-nätverk", där globala lågupplösta höjddata och permanenta vattenmasker smälts samman med Sentinel-1-data (VV, VH). ’Attentive U-Net’ ger 67,2% Intersection Over Union (IoU), och ’Fusion-nätverket’ gav 69,5% IoU på Sen1Floods11-datauppsättningen. Prestandavinsten är 3 till 5% IoU med avseende på de befintliga övervakade modellerna som FCNN (49,3% IoUpoäng), U2Net (62% IoU-poäng) och BASNet (64% IoU) Göra). Kvantitativt visar de två föreslagna nätverken betydande förbättringar jämfört med vi benchmarkmetoder som visar deras potential. Den kvalitativa analysen visar bidraget från lågupplöst höjd och en permanent vattenmask för att förbättra översvämningsdetektering. Ablationsexperiment klargör ytterligare effektiviteten av ratio, självuppmärksamhet, ratio och olika designval som gjorts i föreslagna nätverk.

Dessutom, för att förbättra översvämningsdetekteringsmodellens generaliserbarhet över regioner och för att eliminera beroendet av etiketter, presenteras en ny oövervakad CD-modell som upptäcker översvämningar som ändringar på SAR-tidsseriedata. Den föreslagna modellen är tränad för att lära sig rumsliga egenskaper hos SAR-tidsseriedata med hjälp av oövervakade inlärningstekniker, rekonstruktion och kontrastiv inlärning. Förändringskartorna genereras med en ny algoritm som använder de inlärda latenta egenskapersfördelningarna av data före och efter översvämningen. Modellen uppnådde ett genomsnitt på 70% IoU-poäng, vilket överträffade befintliga översvämningsdetekteringsmodeller som RaVAEn (45,03% IoU-poäng), cGAN (51,49% IoUpoäng) och SCCN (54,87% IoU-poäng) med en betydande minimimarginal på 15% IoU-poäng. Den föreslagna modellen är testad för generaliserbarhet och överträffade de övervakade modellerna ADS-Net och DAUSAR när den testades på osynliga CEMS-översvämningsplatser. Dessutom föreslås ett ramverk för automatisk förändringsövervakning och ändringspunktsdetektering. Ramverket är baserat på den föreslagna oövervakade CD-modellen där tidsseriedata bearbetas genom modellen för att identifiera procentuell förändring vid varje tidsstämpel och ändringspunkten detekteras genom att identifiera det datum då betydande förändringar började återspeglas i SAR-data. När det är integrerat med hög tidsdata, dvs. dagliga bilder från ICEYE, kan ramverket hjälpa till med kontinuerlig översvämningsövervakning och tidig upptäckt av långsamt pågående katastrofhändelser, vilket ger mer tid för respons.

Sammantaget bidrar denna avhandling med övervakade och oövervakade översvämningsdetekteringsmodeller, vilket möjliggör omfattande och allmänt användbar översvämningskartläggning och övervakningskapacitet. Dessa framsteg underlättar katastrofhantering i nästan realtid och en motståndskraftig stadsutveckling och bidrar på så sätt till SDG 11 - Hållbara städer och samhällen.

Due to climate and land-use change, natural disasters such as flooding have been increasing in recent years. Timely and reliable flood detection and mapping can help emergency response and disaster management. In this work, we propose a flood detection network using bi-temporal SAR acquisitions. The proposed segmentation network has an encoder-decoder architecture with two Siamese encoders for pre and post-flood images. The network’s feature maps are fused and enhanced using attention blocks to achieve more accurate detection of the flooded areas. Our proposed network is evaluated on publicly available Sen1Flood11 [1] benchmark dataset. The network outperformed the existing state-of-the-art (uni-temporal) flood detection method by 6% IOU. The experiments highlight that the combination of bi-temporal SAR data with an effective network architecture achieves more accurate flood detection than uni-temporal methods.

Building change detection is essential for monitoring urbanization, disaster assessment, urban planning and frequently updating the maps. 3D structure information from airborne light detection and ranging (LiDAR) is very effective for detecting urban changes. But the 3D point cloud from airborne LiDAR(ALS) holds an enormous amount of unordered and irregularly sparse information. Handling such data is tricky and consumes large memory for processing. Most of this information is not necessary when we are looking for a particular type of urban change. In this study, we propose an automatic method that reduces the 3D point clouds into a much smaller representation without losing necessary information required for detecting Building changes. The method utilizes the Deep Learning(DL) model U-Net for segmenting the buildings from the background. Produced segmentation maps are then processed further for detecting changes and the results are refined using morphological methods. For the change detection task, we used multi-temporal airborne LiDAR data. The data is acquired over Stockholm in the years 2017 and 2019. The changes in buildings are classified into four types: ‘newly built’, ‘demolished’, ‘taller’ and ’shorter’. The detected changes are visualized in one map for better interpretation.

Floods are occurring across the globe, and due to climate change, flood events are expected to increase in the coming years. Current situations urge more focus on efficient monitoring of floods and detecting impacted areas. In this study, we propose two segmentation networks for flood detection on uni-temporal Sentinel-1 Synthetic Aperture Radar data. The first network is “Attentive U-Net”. It takes VV, VH, and the ratio VV/VH as input. The network uses spatial and channel-wise attention to enhance feature maps which help in learning better segmentation. “Attentive U-Net” yields 67% Intersection Over Union (IoU) on the Sen1Floods11 dataset, which is 3% better than the benchmark IoU. The second proposed network is a dual-stream “Fusion network”, where we fuse global low-resolution elevation data and permanent water masks with Sentinel-1 (VV, VH) data. Compared to the previous benchmark on the Sen1Floods11 dataset, our fusion network gave a 4.5% better IoU score. Quantitatively, the performance improvement of both proposed methods is considerable. The quantitative comparison with the benchmark method demonstrates the potential of our proposed flood detection networks. The results are further validated by qualitative analysis, in which we demonstrate that the addition of a low-resolution elevation and a permanent water mask enhances the flood detection results. Through ablation experiments and analysis we also demonstrate the effectiveness of various design choices in proposed networks.