Multi-Stage #SemanticSegmentation Quantifies #Fragmentation of Small #Habitats at #Landscape Scale
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doi.org/10.3390/rs15...
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H/T @david-alexander.bsky.social
#GIS #spatial #mapping #remotesensing #landcover #AI #CNN #conservation #planning #management #machinelearning #PeakDistrict #UK #England
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doi.org/10.3390/rs15...
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H/T @david-alexander.bsky.social
#GIS #spatial #mapping #remotesensing #landcover #AI #CNN #conservation #planning #management #machinelearning #PeakDistrict #UK #England
November 19, 2024 at 10:13 PM
Multi-Stage #SemanticSegmentation Quantifies #Fragmentation of Small #Habitats at #Landscape Scale
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doi.org/10.3390/rs15...
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H/T @david-alexander.bsky.social
#GIS #spatial #mapping #remotesensing #landcover #AI #CNN #conservation #planning #management #machinelearning #PeakDistrict #UK #England
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doi.org/10.3390/rs15...
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H/T @david-alexander.bsky.social
#GIS #spatial #mapping #remotesensing #landcover #AI #CNN #conservation #planning #management #machinelearning #PeakDistrict #UK #England
New paper out! It's an overview of deep learning methods for 2D semantic segmentation with benchmarks, datasets, metrics, and insights into future directions.
Acks ➡️ Lusófona University, FCT, CTS/UNINOVA, LASIGE/FCUL
#semanticsegmentation #deeplearning #2dsegmentation
www.mdpi.com/2673-4117/6/...
Acks ➡️ Lusófona University, FCT, CTS/UNINOVA, LASIGE/FCUL
#semanticsegmentation #deeplearning #2dsegmentation
www.mdpi.com/2673-4117/6/...
www.mdpi.com
July 18, 2025 at 2:27 PM
New paper out! It's an overview of deep learning methods for 2D semantic segmentation with benchmarks, datasets, metrics, and insights into future directions.
Acks ➡️ Lusófona University, FCT, CTS/UNINOVA, LASIGE/FCUL
#semanticsegmentation #deeplearning #2dsegmentation
www.mdpi.com/2673-4117/6/...
Acks ➡️ Lusófona University, FCT, CTS/UNINOVA, LASIGE/FCUL
#semanticsegmentation #deeplearning #2dsegmentation
www.mdpi.com/2673-4117/6/...
A lightweight MM SAM-Adapter adds multimodal cues to the Segment Anything Model, delivering state-of-the-art results on DeLiVER, FMB and MUSES. Read more: https://getnews.me/multimodal-sam-adapter-boosts-robust-semantic-segmentation/ #multimodal #semanticsegmentation
September 17, 2025 at 6:50 AM
A lightweight MM SAM-Adapter adds multimodal cues to the Segment Anything Model, delivering state-of-the-art results on DeLiVER, FMB and MUSES. Read more: https://getnews.me/multimodal-sam-adapter-boosts-robust-semantic-segmentation/ #multimodal #semanticsegmentation
Semantic vs. Instance vs. Panoptic Segmentation explained! Learn the key differences and how expert data annotation supports accurate computer vision and AI training data.
www.habiledata.com/blog/semanti...
#semanticsegmentation #panopticsegmentation #instancesegmentation #dataannotation
www.habiledata.com/blog/semanti...
#semanticsegmentation #panopticsegmentation #instancesegmentation #dataannotation
September 9, 2026 at 1:25 PM
Semantic vs. Instance vs. Panoptic Segmentation explained! Learn the key differences and how expert data annotation supports accurate computer vision and AI training data.
www.habiledata.com/blog/semanti...
#semanticsegmentation #panopticsegmentation #instancesegmentation #dataannotation
www.habiledata.com/blog/semanti...
#semanticsegmentation #panopticsegmentation #instancesegmentation #dataannotation
📄🔓 'SDFSeg: multiscale perception and deformable feature fusion for coastal ecosystem' - an article published in 𝘐𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘵 𝘔𝘢𝘳𝘪𝘯𝘦 𝘛𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺 𝘢𝘯𝘥 𝘚𝘺𝘴𝘵𝘦𝘮𝘴 on #ScienceOpen -
🖇️ #MarinePerception #CoastalEcosystems #SemanticSegmentation #DeepLearning
🖇️ #MarinePerception #CoastalEcosystems #SemanticSegmentation #DeepLearning
SDFSeg: multiscale perception and deformable feature fusion for coastal ecosystem
<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d10530440e150">Monitoring coastal ecosystems is essential for mitigating pollution, preserving biodiversity,
and und...
www.scienceopen.com
August 12, 2025 at 1:06 PM
📄🔓 'SDFSeg: multiscale perception and deformable feature fusion for coastal ecosystem' - an article published in 𝘐𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘵 𝘔𝘢𝘳𝘪𝘯𝘦 𝘛𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺 𝘢𝘯𝘥 𝘚𝘺𝘴𝘵𝘦𝘮𝘴 on #ScienceOpen -
🖇️ #MarinePerception #CoastalEcosystems #SemanticSegmentation #DeepLearning
🖇️ #MarinePerception #CoastalEcosystems #SemanticSegmentation #DeepLearning
Survey finds foundation models pretrained on large image collections boost semantic segmentation, achieving higher intersection‑over‑union on unseen domains. Read more: https://getnews.me/ai-foundations-advance-domain-generalization-for-semantic-segmentation/ #dg #semanticsegmentation
October 7, 2025 at 4:33 PM
Survey finds foundation models pretrained on large image collections boost semantic segmentation, achieving higher intersection‑over‑union on unseen domains. Read more: https://getnews.me/ai-foundations-advance-domain-generalization-for-semantic-segmentation/ #dg #semanticsegmentation
VPNeXt, a simplified Vision Transformer model, beats the prior state‑of‑the‑art mIoU on the VOC2012 benchmark, with its version 3 released on September 27 2025. Read more: https://getnews.me/vpnext-rethinks-dense-decoding-for-vision-transformers/ #vpnext #visiontransformer #semanticsegmentation
October 1, 2025 at 11:34 AM
VPNeXt, a simplified Vision Transformer model, beats the prior state‑of‑the‑art mIoU on the VOC2012 benchmark, with its version 3 released on September 27 2025. Read more: https://getnews.me/vpnext-rethinks-dense-decoding-for-vision-transformers/ #vpnext #visiontransformer #semanticsegmentation
Polarimetric imaging added to RGB segmentation raises mean IoU and cuts contour error for floating debris, shown on the PoTATO dataset of plastic bottles. Read more: https://getnews.me/polarimetric-fusion-boosts-semantic-segmentation-of-water-debris/ #polarimetric #semanticsegmentation
September 30, 2025 at 11:29 PM
Polarimetric imaging added to RGB segmentation raises mean IoU and cuts contour error for floating debris, shown on the PoTATO dataset of plastic bottles. Read more: https://getnews.me/polarimetric-fusion-boosts-semantic-segmentation-of-water-debris/ #polarimetric #semanticsegmentation
GLCND.IO
#3DSegmentation #SemanticSegmentation #PointCloud
https://glcnd.io/advancements-in-3d-segmentation-for-precise-data-analysis/
#3DSegmentation #SemanticSegmentation #PointCloud
https://glcnd.io/advancements-in-3d-segmentation-for-precise-data-analysis/
Advancements in 3D segmentation for precise data analysis - GLCND.IO
glcnd.io
April 8, 2026 at 11:54 PM
GLCND.IO
#3DSegmentation #SemanticSegmentation #PointCloud
https://glcnd.io/advancements-in-3d-segmentation-for-precise-data-analysis/
#3DSegmentation #SemanticSegmentation #PointCloud
https://glcnd.io/advancements-in-3d-segmentation-for-precise-data-analysis/
Compared to previous lifting techniques, 3DIML achieves quick, precise 3D instance segmentation—25× faster. #semanticsegmentation
Make Class-Agnostic 3D Segmentation Efficient with 3DIML
hackernoon.com
October 24, 2025 at 3:34 PM
Compared to previous lifting techniques, 3DIML achieves quick, precise 3D instance segmentation—25× faster. #semanticsegmentation
3DIML uses quick mapping, lifting, and localization modules to transform 2D masks into consistent 3D labels. #semanticsegmentation
Consistent 3D Mask Labeling Made Simple
hackernoon.com
October 24, 2025 at 3:34 PM
3DIML uses quick mapping, lifting, and localization modules to transform 2D masks into consistent 3D labels. #semanticsegmentation
Compared to previous neural field techniques, 3DIML achieves 14–24× faster training times for 3D instance segmentation from 2D photos. #semanticsegmentation
Solving 3D Segmentation’s Biggest Bottleneck
hackernoon.com
October 24, 2025 at 3:33 PM
Compared to previous neural field techniques, 3DIML achieves 14–24× faster training times for 3D instance segmentation from 2D photos. #semanticsegmentation
Analysing non-linearities and threshold effects between street-level built environments and local #crime patterns: An interpretable machine learning approach by Sugie Lee et al
buff.ly/JHLcL9c
#GoogleStreetView #SemanticSegmentation
buff.ly/JHLcL9c
#GoogleStreetView #SemanticSegmentation
April 23, 2025 at 9:01 AM
Analysing non-linearities and threshold effects between street-level built environments and local #crime patterns: An interpretable machine learning approach by Sugie Lee et al
buff.ly/JHLcL9c
#GoogleStreetView #SemanticSegmentation
buff.ly/JHLcL9c
#GoogleStreetView #SemanticSegmentation
Pingcheng Dong, et al.: A 28nm 0.22 {\mu}J/token memory-compute-intensity-aware CNN-Transformer accelerator with hybrid-attention-based layer-fusion and cascaded pruning for semanticsegmentation https://arxiv.org/abs/2512.17555 https://arxiv.org/pdf/2512.17555 https://arxiv.org/html/2512.17555
December 22, 2025 at 6:35 AM
Pingcheng Dong, et al.: A 28nm 0.22 {\mu}J/token memory-compute-intensity-aware CNN-Transformer accelerator with hybrid-attention-based layer-fusion and cascaded pruning for semanticsegmentation https://arxiv.org/abs/2512.17555 https://arxiv.org/pdf/2512.17555 https://arxiv.org/html/2512.17555
Bounding boxes often pull in the background, so models learn noise along with the signal. Segmentation labels every pixel, keeping boundaries clean and improving performance in overlap and occlusion. Read more: bit.ly/4d4AHaF
#ComputerVision #SemanticSegmentation #AI
#ComputerVision #SemanticSegmentation #AI
May 5, 2026 at 10:18 AM
Bounding boxes often pull in the background, so models learn noise along with the signal. Segmentation labels every pixel, keeping boundaries clean and improving performance in overlap and occlusion. Read more: bit.ly/4d4AHaF
#ComputerVision #SemanticSegmentation #AI
#ComputerVision #SemanticSegmentation #AI