%PDF-1.4 % 1 0 obj << /Type /Pages /Count 1 /Kids [ 2 0 R ] >> endobj 2 0 obj << /Type /Page /Parent 1 0 R /Resources << /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] /Font << /QuickPDFF48b12b39 70 0 R /QuickPDFFb5c4a36e 116 0 R >> >> /Contents [ 5 0 R ] /MediaBox [ 0 0 595.2756 841.8898 ] /CropBox [ 0 0 595.2756 841.8898 ] >> endobj 3 0 obj << /Type /Catalog /Pages 1 0 R /Metadata 117 0 R >> endobj 4 0 obj << /Producer (Debenu Quick PDF Library 11.15 \(www.debenu.com\)) /Creator (Debenu Quick PDF Library 11.15 \(www.debenu.com\)) /CreationDate (D:20220919115604+02'00') /ModDate (D:20220919115604+02'00') >> endobj 5 0 obj << /Length 3029 /LC /iSQP >> stream 0 Tr /QuickPDFFb5c4a36e 14 Tf 0 0 0 rg 100 Tz 0 Tw 0 Tc 0 Ts BT 1 0 0 1 141.3138 770.8329 Tm (Automated Building Extraction from Aerial Images )Tj 1 0 0 1 104.1508 752.1709 Tm (with An Improved End-To-End Deep-Learning-Based Approach)Tj ET /QuickPDFFb5c4a36e 11 Tf 0 0 0 rg BT 1 0 0 1 197.8513 717.2179 Tm (Hailun Yan and Ruisheng Wang \(Canada\))Tj ET /QuickPDFFb5c4a36e 11 Tf 0 0 0 rg BT 1 0 0 1 56.6929 675.188 Tm 14.663 TL (Key words: )' ET /QuickPDFF48b12b39 11 Tf 0 0 0 rg BT 1 0 0 1 141.7323 675.188 Tm 14.454 TL (Land management; Photogrammetry; Remote sensing; Spatial planning; Building)' (extraction; Automation; Segmentation; Vectorization; Land use and land )' (cover mapping)' ET /QuickPDFFb5c4a36e 11 Tf 0 0 0 rg BT 1 0 0 1 56.6929 618.4951 Tm 14.663 TL (SUMMARY)' ET /QuickPDFF48b12b39 11 Tf 0 0 0 rg BT 1 0 0 1 56.6929 590.1487 Tm 14.454 TL (Automatically extracting high quality building polygons from satellite images is crucial for supporting land)' (use and land cover mapping as the traditional object extraction process requires human image interpreters)' (which is labor intensive and time consuming. This paper adopts the state-of-the-art Swin Transformer neural)' (network as the backbone of Mask R-CNN to precisely segment building instances. Since the polygons)' (directly converted from instance segmentations often differ from real-world building footprints which)' (usually have straight edges and right angles, they are undesired for many cartographic and engineering)' (applications. Hence, a building regularization method is presented to produce regularized and precise)' (building polygons. The building regularization consists of generating polygon hypotheses with a novel)' (hypothesis generation)' ()' (method, and optimizing the hypothesized models using a modified Minimum-Description-Lengthbased)' (method which emphasizes on obtaining low residuals between the predicted and hypothesized polygons with)' (a slight sacrifice to the model simplicity while forming regular shapes. Evaluations of the proposed method)' (on the round 2 dataset of the SpaceNet Building Detection )' ()' (Challenge show that our method surpasses several other state-of-the-art methods in terms of the extraction)' (accuracy and completeness. This work is beneficial for various land use and land cover applications such as)' (urban expansion, city planning, resource management, etc. Our future work is to precisely extract buildings)' (with complex features such as curved edges and non-right-angled )' ()' (corners.)' ET /QuickPDFF48b12b39 10 Tf 0 0 0 rg BT 1 0 0 1 56.6929 163.8497 Tm 13.14 TL (__________________________________________________________________________________________)' ()' (Automated Building Extraction from Aerial Images )' (with An Improved End-To-End Deep-Learning-Based Approach \(11721\))' (Hailun Yan and Ruisheng Wang \(Canada\))' ()' (FIG Congress 2022)' (Volunteering for the future - Geospatial excellence for a better living)' (Warsaw, Poland, 1115 September 2022)' ET endstream endobj 6 0 obj << /Length 316 /Filter /FlateDecode >> stream xMRKnCAۿSpk`y^Uu[jIɒT%C/:b}Rx\%=
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