{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:QPJ5MCJKRGROOVFTOJVGWBPORA","short_pith_number":"pith:QPJ5MCJK","canonical_record":{"source":{"id":"1902.04604","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-12T19:39:07Z","cross_cats_sorted":[],"title_canon_sha256":"8a20e60d2b70f75b49b5ad65bf6bc6940cc3277a2cd6684d8a079eaa5e81c1ec","abstract_canon_sha256":"445cecb78c2a76434bb5695443f0c439e0be5f4a417701b83ee5968a893a4ad9"},"schema_version":"1.0"},"canonical_sha256":"83d3d6092a89a2e754b3726a6b05ee88171018148421c7f6c8d23898b60b4b0a","source":{"kind":"arxiv","id":"1902.04604","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.04604","created_at":"2026-05-17T23:54:06Z"},{"alias_kind":"arxiv_version","alias_value":"1902.04604v1","created_at":"2026-05-17T23:54:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.04604","created_at":"2026-05-17T23:54:06Z"},{"alias_kind":"pith_short_12","alias_value":"QPJ5MCJKRGRO","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"QPJ5MCJKRGROOVFT","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"QPJ5MCJK","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:QPJ5MCJKRGROOVFTOJVGWBPORA","target":"record","payload":{"canonical_record":{"source":{"id":"1902.04604","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-12T19:39:07Z","cross_cats_sorted":[],"title_canon_sha256":"8a20e60d2b70f75b49b5ad65bf6bc6940cc3277a2cd6684d8a079eaa5e81c1ec","abstract_canon_sha256":"445cecb78c2a76434bb5695443f0c439e0be5f4a417701b83ee5968a893a4ad9"},"schema_version":"1.0"},"canonical_sha256":"83d3d6092a89a2e754b3726a6b05ee88171018148421c7f6c8d23898b60b4b0a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:06.473767Z","signature_b64":"CGP7ltR/SvHqYjE4XBYBFf52pnV5I5Dk8j74FlWgAtcoWJ3pEZLrXlWPfcVS6q8+RDZNjDx5AEJHH3d1Fv3JDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83d3d6092a89a2e754b3726a6b05ee88171018148421c7f6c8d23898b60b4b0a","last_reissued_at":"2026-05-17T23:54:06.473163Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:06.473163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.04604","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:54:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eknww5NwTwzEqQ3RxD+ou7wKud5y3byynoGG3uIw11ccqSwSWX7uh6gh7gECYbGDTb+Pyz6d7qMKh+dMrD/2DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T15:54:00.911820Z"},"content_sha256":"3113acab91742dab417a07e794075aaf63c6a89aa68803451e588a8014d2d97c","schema_version":"1.0","event_id":"sha256:3113acab91742dab417a07e794075aaf63c6a89aa68803451e588a8014d2d97c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:QPJ5MCJKRGROOVFTOJVGWBPORA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrew Michaelis, Auroop Ganguly, Edward Collier, Gayaka Shreekant, Geri Madanguit, Kate Duffy, Ramakrishna Nemani, Sangram Ganguly, Shuang Li, Subodh Kalia, Supratik Mukhopadhyay","submitted_at":"2019-02-12T19:39:07Z","abstract_excerpt":"Machine learning has proven to be useful in classification and segmentation of images. In this paper, we evaluate a training methodology for pixel-wise segmentation on high resolution satellite images using progressive growing of generative adversarial networks. We apply our model to segmenting building rooftops and compare these results to conventional methods for rooftop segmentation. We present our findings using the SpaceNet version 2 dataset. Progressive GAN training achieved a test accuracy of 93% compared to 89% for traditional GAN training."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.04604","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:54:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"td3PdlTGMjZMID6pATM4kP9YGFxDXGHLnaTJXCfqHuKhKUE4PaID2wWZ66wwOgp7gce1Djq9mqQAApMMu2nvCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T15:54:00.912516Z"},"content_sha256":"5483e807c118ac796a154885327a96d2508bfce7191ec3fb3bac0eca420434f8","schema_version":"1.0","event_id":"sha256:5483e807c118ac796a154885327a96d2508bfce7191ec3fb3bac0eca420434f8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QPJ5MCJKRGROOVFTOJVGWBPORA/bundle.json","state_url":"https://pith.science/pith/QPJ5MCJKRGROOVFTOJVGWBPORA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QPJ5MCJKRGROOVFTOJVGWBPORA/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-29T15:54:00Z","links":{"resolver":"https://pith.science/pith/QPJ5MCJKRGROOVFTOJVGWBPORA","bundle":"https://pith.science/pith/QPJ5MCJKRGROOVFTOJVGWBPORA/bundle.json","state":"https://pith.science/pith/QPJ5MCJKRGROOVFTOJVGWBPORA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QPJ5MCJKRGROOVFTOJVGWBPORA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:QPJ5MCJKRGROOVFTOJVGWBPORA","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"445cecb78c2a76434bb5695443f0c439e0be5f4a417701b83ee5968a893a4ad9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-12T19:39:07Z","title_canon_sha256":"8a20e60d2b70f75b49b5ad65bf6bc6940cc3277a2cd6684d8a079eaa5e81c1ec"},"schema_version":"1.0","source":{"id":"1902.04604","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.04604","created_at":"2026-05-17T23:54:06Z"},{"alias_kind":"arxiv_version","alias_value":"1902.04604v1","created_at":"2026-05-17T23:54:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.04604","created_at":"2026-05-17T23:54:06Z"},{"alias_kind":"pith_short_12","alias_value":"QPJ5MCJKRGRO","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"QPJ5MCJKRGROOVFT","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"QPJ5MCJK","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:5483e807c118ac796a154885327a96d2508bfce7191ec3fb3bac0eca420434f8","target":"graph","created_at":"2026-05-17T23:54:06Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Machine learning has proven to be useful in classification and segmentation of images. In this paper, we evaluate a training methodology for pixel-wise segmentation on high resolution satellite images using progressive growing of generative adversarial networks. We apply our model to segmenting building rooftops and compare these results to conventional methods for rooftop segmentation. We present our findings using the SpaceNet version 2 dataset. Progressive GAN training achieved a test accuracy of 93% compared to 89% for traditional GAN training.","authors_text":"Andrew Michaelis, Auroop Ganguly, Edward Collier, Gayaka Shreekant, Geri Madanguit, Kate Duffy, Ramakrishna Nemani, Sangram Ganguly, Shuang Li, Subodh Kalia, Supratik Mukhopadhyay","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-12T19:39:07Z","title":"Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.04604","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3113acab91742dab417a07e794075aaf63c6a89aa68803451e588a8014d2d97c","target":"record","created_at":"2026-05-17T23:54:06Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"445cecb78c2a76434bb5695443f0c439e0be5f4a417701b83ee5968a893a4ad9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-12T19:39:07Z","title_canon_sha256":"8a20e60d2b70f75b49b5ad65bf6bc6940cc3277a2cd6684d8a079eaa5e81c1ec"},"schema_version":"1.0","source":{"id":"1902.04604","kind":"arxiv","version":1}},"canonical_sha256":"83d3d6092a89a2e754b3726a6b05ee88171018148421c7f6c8d23898b60b4b0a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"83d3d6092a89a2e754b3726a6b05ee88171018148421c7f6c8d23898b60b4b0a","first_computed_at":"2026-05-17T23:54:06.473163Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:54:06.473163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CGP7ltR/SvHqYjE4XBYBFf52pnV5I5Dk8j74FlWgAtcoWJ3pEZLrXlWPfcVS6q8+RDZNjDx5AEJHH3d1Fv3JDg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:54:06.473767Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.04604","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3113acab91742dab417a07e794075aaf63c6a89aa68803451e588a8014d2d97c","sha256:5483e807c118ac796a154885327a96d2508bfce7191ec3fb3bac0eca420434f8"],"state_sha256":"7fa776d2a4653a4f04d52d544c832eadb9fdb159724f54ec82383380a83c50e3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HVYykApjCT9KZ1pVjovY/in1fr7+80SArB6VzJHe/y+1x3V2Db041MLK1txHDxbFZcK9MuVsMSYYxJTpjiWVCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T15:54:00.916239Z","bundle_sha256":"98ecc02e3c388fc289391cfc8fe27fe6a48d9ed54171af166dc432b912e069b9"}}