---
title: 'DelAnyFlow: Unified Delineation Across Domains'
url: https://www.emergentmind.com/topics/delineate-anything-flow-delanyflow
type: topic
---

# DelAnyFlow: Unified Delineation Across Domains

Delineate Anything Flow (DelAnyFlow) is a label that refers to multiple technically distinct constructs in the cited literature rather than to a single universally fixed method. In the 2025 agricultural remote-sensing literature, DelAnyFlow denotes a resolution-agnostic pipeline for large-scale field boundary mapping that couples the DelAny instance-segmentation model with structured post-processing, tile merging, and vectorization to produce topologically consistent vector boundaries [2511.13417]. In other supplied technical summaries, the same label is also used for two-frame optical-flow systems that augment a flow backbone with Segment Anything Model (SAM) features or SAM-guided training mechanisms to reduce motion fragmentation [2307.16586] [2405.02608]. A further, broader usage appears in a diagrammatic adaptation of Unified Control and Data Flow Diagrams, where “DelAnyFlow” names a generalized notation for describing arbitrarily complex systems through unified control flow and data flow [1610.02374]. Taken together, these sources establish DelAnyFlow as an overloaded term spanning diagrammatic system representation, dense motion estimation, and agricultural field delineation.

## 1. Terminological scope and historiography

The name DelAnyFlow is not introduced in a single canonical paper across all domains represented here. Instead, the supplied corpus documents at least three distinct usages.

In the software- and systems-modeling context, the term is attached to a generalized application of Unified Control and Data Flow Diagrams (UCD). The underlying method describes systems through control-flow relations, data-flow relations, strict system boundaries, and explicitly separated timelines, and the summary states that the same conventions can be used to build a “Delineate Anything Flow” diagram for arbitrarily complex systems [1610.02374]. In this usage, DelAnyFlow is a diagramming language or representational scheme rather than a learned model.

In optical flow, one supplied summary explicitly recasts the architecture of SAMFlow as a recipe for building “Delineate Anything Flow (DelAnyFlow),” defined as a two-frame optical-flow estimator that plugs a frozen SAM image encoder into FlowFormer, fuses and adapts the resulting features via the Context Fusion Module (CFM) and Context Adaptation Module (CAM), and trains under standard optical-flow losses to eliminate fragmentation [2307.16586]. Another summary identifies “DelAnyFlow or UnSAMFlow” with an unsupervised optical-flow system based on ARFlow, augmented by a semantic augmentation module, a homography-based smoothness loss, and an optional mask-feature branch using SAM masks [2405.02608]. These two optical-flow formulations are not identical architectures.

In agricultural remote sensing, DelAnyFlow is presented as a turnkey, resolution-agnostic pipeline for extracting topologically consistent agricultural field boundaries at regional to national scales. Its core is DelAny, an instance-segmentation model based on the YOLOv11 family and trained on FBIS-22M, followed by a five-stage post-processing and vectorization pipeline [2511.13417]. The related DelAny paper also uses “DelAnyFlow” to denote the end-to-end field-boundary delineation pipeline built from normalization, YOLOv11-style instance segmentation, and contour extraction [2504.02534].

A common misconception is that DelAnyFlow denotes a single architecture across domains. The supplied sources do not support that interpretation. A more accurate reading is that DelAnyFlow functions as a reusable label for “delineate anything” workflows in multiple technical settings.

## 2. Diagrammatic DelAnyFlow from unified control and data flow

In the UCD-derived usage, DelAnyFlow is grounded in an explicit formalization of process blocks, data holders, and two relations: \(CF \subseteq P \times P\), the control-flow relation, and \(DF \subseteq (P \cup D) \times (P \cup D)\), the data-flow relation [1610.02374]. The summary defines
\[
CF = \{(p_i,p_j)\mid \text{control passes from } p_i \text{ to } p_j\},
\]
and
\[
DF = \{(x,y)\mid \text{information flows from } x \text{ to } y\}.
\]
Control flow is denoted by a thin, straight, angled arrow with an optional timeline token, while data flow is denoted by a thick, curved arrow [1610.02374].

The graphical vocabulary is explicit. Rectangle denotes a process or code block; rectangle with double-struck vertical edges denotes a module, class, file, library, or equipment; ellipse denotes a data holder; wavy-bottom shape denotes a file or database; paper shape denotes a document or human-generated report; rhombus denotes an algorithmic decision; and a small rectangle labeled “OR” denotes a logical choice point [1610.02374]. Additional relations include “has” or part-of, alias or type-of via the “is” line, and labeled create/delete operations [1610.02374].

A central construct is the timeline. The summary defines a timeline as the mechanism by which control is distributed and states that it is always drawn as a thin straight or angled arrow carrying a small rectangle labeled with a sequence number or timestamp. A process may emit a new timeline arrow to denote a sub-timeline, allowing arbitrary nesting depth [1610.02374]. This explicit separation between blocks of code and timelines is presented as the means by which nesting of the control flow can be shown “as deep as necessary” [1610.02374].

The supplied examples illustrate the intended generality. One example represents a simple C++ function call using timeline tokens, data-flow arrows for variable passing, and return control flow; another represents a production-line station flow with module boundaries, a left-to-right control timeline, and data updates to a widget plus database writes [1610.02374]. The adaptation rule is stated directly: use the identical palette of shapes and lines to model any system, with control flow interpreted as “resource/time allocation,” data flow as “material/information transfer,” modules as “subsystems,” and data holders as “buffers” or “containers” [1610.02374].

This suggests that the diagrammatic DelAnyFlow is best understood as a unifying notation for heterogeneous systems rather than as a domain-specific algorithm.

## 3. DelAnyFlow in supervised optical flow via SAM-enhanced FlowFormer

One optical-flow formulation of DelAnyFlow builds on FlowFormer by adding the frozen image encoder from SAM [2307.16586]. Each input frame \(I \in \mathbb{R}^{H \times W \times 3}\) is processed by a SAM image encoder \(E_S\), described as a ViT-backbone with frozen weights, producing high-level object-aware features \(\Phi_S\), and by a FlowFormer context encoder \(E_F\), described as a lightweight CNN producing low-level flow-context features \(\Phi_F\) [2307.16586]. These are merged in the Context Fusion Module into \(\Phi_C\), refined in the Context Adaptation Module into \(\Phi_C^A\), and then passed to the standard FlowFormer correlation-decoder and update-block stack to produce the final optical flow field \(F \in \mathbb{R}^{H \times W \times 2}\) [2307.16586].

The Context Fusion Module concatenates SAM and FlowFormer features and passes them through two residual conv-blocks. The summary specifies that \(\Phi_\parallel = [\Phi_S \parallel \Phi_F]\), with \(Conv_k\) denoting standard \(3 \times 3\) convolutions and \(\Delta\) a \(3 \times 3\) depthwise convolution. The result is a fused feature tensor \(\Phi_C\) carrying both integrity-aware object features from SAM and fine-grained local flow cues from the original encoder [2307.16586].

The Context Adaptation Module addresses the mismatch between SAM pre-training for segmentation and the optical-flow objective. It introduces \(K\) Learned Task-Specific Embedding tokens \(\Omega_T \in \mathbb{R}^{K \times d_C}\), randomly initialized and learned end-to-end, and applies one or more Two-Way Attention blocks consisting of self-attention on \(\Omega_T\), cross-attention from \(\overline{\Omega}_T\) to \(\Phi_C\), an MLP-based embed-update, and cross-attention from \(\Phi_C\) to \(\Omega_U\) to yield \(\Phi_C^A\) [2307.16586]. Positional encoding is cropped from SAM’s original positional encoding to match feature-map size [2307.16586].

Training is described as largely following FlowFormer’s two-stage regimen, while the summary actually enumerates three stages and states that SAM is frozen at all times. Stage 1 uses FlyingThings3D only for 120 K steps with batch size 3, crop size \(432 \times 960\), random scale in \([0.8,1.2]\), random horizontal/vertical flips, AdamW with \(\eta = 1.25 \times 10^{-4}\), \(\beta = (0.9,0.999)\), \(wd = 10^{-5}\), one-cycle learning-rate decay, and gradient clipping at 1.0. Stage 2 uses a data mixture of FlyingThings3D, MPI-Sintel, KITTI-15, and HD1K for 240 K steps with batch size 3 and grad-accum \(= 2\), with \(\eta = 5 \times 10^{-6}\). Stage 3 uses KITTI-15 only for 50 K steps with batch size 3 and the same learning rate. Hardware is listed as 3 RTX 3090 GPUs with mixed-precision training [2307.16586].

The loss terms are the Endpoint Error
\[
\mathcal{L}_{EPE} = \frac{1}{HW}\sum_{x,y}\left\lVert F_{\text{pred}(x,y)} - F_{\text{gt}(x,y)} \right\rVert_2
\]
and an optional Robust Charbonnier / \(L1\) term with \(\epsilon = 10^{-3}\), with total loss \(\mathcal{L} = \mathcal{L}_{EPE}\) or plus \(\alpha \mathcal{L}_{charb}\) if desired [2307.16586].

Quantitatively, the summary reports on Sintel(train) and KITTI-15(train) that the FlowFormer baseline obtains \(0.94/2.33\) EPE on Sintel clean/final and \(4.09\) EPE / \(14.72\) F1-all on KITTI, whereas DelAnyFlow obtains \(0.86/2.10\) EPE on Sintel and \(3.55\) EPE / \(12.32\) F1 on KITTI, corresponding to reductions of \(8.5\%/9.9\%\) and \(13.2\%/16.3\%\), respectively [2307.16586]. On Sintel(test) and KITTI-15(test), the summary gives FlowFormer++ at \(1.07/1.94\) EPE clean/final and \(4.52\) F1, and DelAnyFlow at \(1.00/2.08\) and \(4.49\) F1 [2307.16586]. It further states that the method yields an 18–19% EPE drop in occluded regions [2307.16586].

The stated mechanism is that fragmentation in flow arises when models rely on purely local photometric cues, while SAM’s ViT encoder encodes global object integrity across occlusions, texture-poor regions, or brightness changes; CFM propagates full-object awareness to pixel context features, and CAM injects task-specific motion priors via LTSE tokens and cross-attention [2307.16586].

## 4. DelAnyFlow in unsupervised optical flow via SAM-guided ARFlow

A second optical-flow usage equates DelAnyFlow with the UnSAMFlow formulation, an unsupervised network guided by SAM [2405.02608]. Here the backbone is an encoder-decoder adapted from ARFlow, not FlowFormer. The encoder constructs a 6-level fully convolutional pyramid \(\{f_t^{(2)},\dots,f_t^{(6)}\}\), where level \(l\) has spatial size \(\frac{H}{2^l} \times \frac{W}{2^l}\) and channel dimension 32, with all convolutions \(3 \times 3\), stride 2 on downsample layers and stride 1 otherwise [2405.02608].

The iterative decoder begins from the coarsest estimate \(\hat F_{1 \to 2}^{(7)} = 0\) and, for \(l = 6,5,\dots,2\), upsamples the previous estimate, warps \(f_2^{(l)}\), computes a \(9 \times 9\) correlation window yielding an 81-dimensional cost, concatenates the upsampled flow, the cost volume, and a \(1 \times 1\)-convolved feature tensor, then predicts a residual flow through a small Res-block flow estimator and further refines it through a context net [2405.02608]. Final full-resolution flow is obtained by upsampling \(\hat F^{(2)}\) by factor 4 with learned convex weights [2405.02608].

An optional mask-feature module inputs SAM masks \(M_t \in \{0,1\}^{n_t \times H \times W}\). The masks are converted to a full segmentation \(\mathcal{S} \in \{1,\dots,K\}^{H \times W}\) by sorting masks by ascending area, assigning overlapping pixels to the smallest-mask identifier, and labeling uncovered pixels as background [2405.02608]. At each pyramid level, the method downsamples \(\mathcal{S}\), applies a \(1 \times 1\) convolution to image features, computes regionwise max-pooled feature representatives \(m_k\), broadcasts them back to all pixels of region \(k\), concatenates regionwise and local features, and applies another \(1 \times 1\) convolution to obtain mask features \(g_t^{(l)}\), which are then warped and correlated like the image features [2405.02608].

Two SAM-guided training mechanisms distinguish this formulation. The semantic augmentation module imposes self-supervised consistency under transformation using known augmentations \(\mathcal{T}_1,\mathcal{T}_2\), including photometric transforms, 2D affine transforms, and semantic copy-paste of key-object masks from different samples. The augmentation loss is
\[
\ell_{\rm aug} = \sum_{p \notin \mathrm{occl}} \left\|\hat F_{1\to2}^{\rm aug}(p) - \tilde F_{1\to2}(p)\right\|_1
\]
with key objects selected to have large mask-overlap [2405.02608]. The homography-based smoothness loss replaces traditional second-order smoothness with a regional approximation: for each SAM segment region \(R\), a homography \(H_R\) is fitted by RANSAC if the inlier rate is at least 50%, and the regionwise loss is
\[
\ell_{\rm hg} = \sum_R \sum_{p \in R} \left\|F_{1\to2}(p) - F_R(p)\right\|_1
\]
where \(F_R(p)\) is the regional warp induced by \(H_R\) [2405.02608].

The full objective is
\[
\ell = \ell_{\rm ph} + w_{\rm aug}\ell_{\rm aug} + w_{\rm hg}\ell_{\rm hg},
\]
with \(w_{\rm aug} = 0.1\) and \(w_{\rm hg} = 0.1\), where \(\ell_{\rm ph}\) is an ARFlow-style photometric loss combining \(L_1\), SSIM, and Census terms [2405.02608]. Implementation details include PyTorch, the ViT-H “default” SAM model, KITTI raw and Sintel raw pretraining followed by fine-tuning, resizing to \(256 \times 832\) for KITTI and \(448 \times 1024\) for Sintel, batch size 8, Adam with \((\beta_1,\beta_2) = (0.9,0.999)\), constant learning rate \(2 \times 10^{-4}\) for 100 K iterations followed by OneCycleLR up to \(4 \times 10^{-4}\) for 100 K iterations, and delayed activation of semantic augmentation and homography loss until 150 K iterations [2405.02608].

Reported results on KITTI-2015(test) show Fl-all improving from \(11.80\%\) for the ARFlow backbone baseline to \(7.83\%\) for the full model with augmentation, homography, and mask features [2405.02608]. On Sintel Final(test), EPE improves from \(5.89\) px to \(5.20\) px [2405.02608]. Cross-domain results are also reported: training on KITTI and testing on Sintel Final yields EPE \(= 5.75\) px with SAM versus \(7.02\) px without SAM, and training on Sintel then testing on KITTI yields Fl-all \(= 7.58\%\) with SAM versus \(8.59\%\) without SAM [2405.02608]. Runtime is given as \(0.0334 \pm 0.0038\) s on \(376 \times 1242\) inputs on Tesla P100, excluding SAM mask extraction, with only \(+0.11\) M added parameters for the mask-feature module [2405.02608].

The existence of both the FlowFormer-based and ARFlow-based optical-flow variants is another indication that “DelAnyFlow” is not a uniquely specified optical-flow architecture in the supplied materials.

## 5. DelAnyFlow for agricultural field boundary detection

In agricultural remote sensing, DelAnyFlow denotes an end-to-end methodology for country-level field boundary detection from satellite imagery [2511.13417]. The central idea is to reframe field-boundary extraction as instance segmentation of individual field objects, then apply structured post-processing to transform raw instance masks into seamless vector polygons [2511.13417] [2504.02534].

The DelAny model at the core of this pipeline is based on the YOLOv11 instance-segmentation family, specialized for multi-resolution satellite imagery [2511.13417]. The full model uses a YOLOv11 backbone with a \(1.5\times\) width multiplier, capped at 512 channels, yielding 379 layers and approximately 62 million parameters. DelAny-S is described as a lightweight variant with half the depth and one-quarter the width, 203 layers, and approximately 2.9 million parameters [2511.13417]. The heads include a single-category class head for “field” using sigmoid activation and focal-style classification loss, a box-regression head using CIoU loss, and a mask head producing \(28 \times 28\) instance masks upsampled to tile resolution [2511.13417].

The related DelAny technical summary presents the pipeline as native-resolution image input, per-channel normalization without forced resizing, a YOLOv11 feature extractor, a multi-scale feature pyramid, a resolution-agnostic design in which the backbone and FPN accept unchanged image sizes and channels while dynamic anchor or objectness heads adapt to varying feature-map strides, and a YOLOv11-style instance-segmentation head followed by non-maximum suppression, thresholding of mask logits, and contour extraction into closed field boundaries [2504.02534]. The summary states explicitly that no fixed spatial resampling is performed, so the network accepts \(\hat I\) of arbitrary \((H,W)\) [2504.02534].

The field-domain DelAnyFlow then applies a five-stage post-processing sequence [2511.13417]. Quality masking excludes pixels flagged as nodata or outside an external land-cover mask such as water, forest, or urban areas, and generates a context mask by buffering excluded zones by 20 m [2511.13417]. Morphological refinement operates per instance in descending area order: erode by a \(3 \times 3\) structuring element, retain only the largest connected component, dilate by the same element, and discard instances smaller than 0.1 ha for high-resolution inputs or 0.5 ha for Sentinel-2 [2511.13417]. Cross-tile unification merges overlapping detections across adjacent \(512 \times 512\) tiles when
\[
\mathrm{IoU}(P_i,P_j) = \frac{|P_i \cap P_j|}{|P_i \cup P_j|}
\]
satisfies IoU \(\ge 0.5\) and overlap area \(\ge \min(5 \text{ ha}, 0.3 \times \text{area of smaller})\) [2511.13417]. Mosaic assembly rasterizes all refined instances to a seamless grid with at most one field per pixel and removes slivers smaller than \(10 \text{ m}^2\) [2511.13417]. Vectorization and topology validation extract contours, ensure closed loops, enforce no overlaps or gaps, remove small polygons, reproject to EPSG:4326, and embed attributes including field ID, area, and tile provenance [2511.13417].

A plausible implication is that the field-domain meaning of “flow” is procedural rather than dynamical: it refers to the sequential movement from instance masks to topology-valid vector boundaries, not to optical flow.

## 6. Data regimes, quantitative performance, and deployment scale

A substantial part of the field-domain DelAnyFlow literature is the dataset regime supporting resolution agnosticism. FBIS-22M is described as the largest field-delineation benchmark to date, with 672,909 image patches and 22.9 million manually validated field masks, spanning resolutions from 0.25 m to 10 m and geographic coverage across Austria, France, Luxembourg, the Netherlands, Slovakia, Slovenia, Spain, Sweden, and Ukraine [2511.13417]. The related DelAny summary gives 22,926,427 individual-field masks and a split of 636,784 training images and 36,125 test images, using COCO-style JSON with one “field” category, bounding boxes, and per-instance segmentation polygons or masks [2504.02534]. The annotation protocol includes official LPIS parcels where available and expert manual delineation on high-resolution imagery elsewhere, followed by quality control to remove slivers, ensure closed polygons, and correct topological errors [2511.13417].

Training settings are reported with high specificity. DelAny and DelAny-S are trained from COCO-pretrained weights for 30 epochs with batch size 320, corresponding to 40 images per GPU across 8 NVIDIA H100s, learning rate \(2 \times 10^{-5}\) decayed by a factor of 0.1 at epochs 20 and 25, and augmentations including random horizontal and vertical flips, color jitter, mixup, copy-paste, and mosaic for the first 20 epochs [2511.13417]. The DelAny summary separately specifies AdamW, initial learning rate \(2 \times 10^{-5}\), exponential decay, 30 epochs, and inference on one \(1024 \times 1024\) crop at \(16.8\) ms for DelAny-S and \(25.0\) ms for DelAny [2504.02534].

The following table consolidates quantitative values stated in the supplied field-domain sources.

| System / setting | Metric | Reported value |
|---|---:|---:|
| DelAny | mAP@0.5 | 0.720 |
| DelAny | mAP@[0.5:0.95] | 0.477 |
| SAM2 | mAP@0.5 | 0.382 |
| SAM2 | mAP@[0.5:0.95] | 0.235 |
| DelAny | Latency per 512×512 tile on A100 | 25 ms |
| DelAny-S | Latency per 512×512 tile on A100 | 16.8 ms |
| Ukraine deployment | Area processed | 603,000 km² |
| Ukraine deployment | End-to-end runtime | 5.4 hours |
| Ukraine, 5 m | Fields detected | 3.75 million |
| Ukraine, 2.5 m | Fields detected | 5.15 million |
| Sinergise Solutions | Fields detected | 2.66 million |
| NASA Harvest (2023) | Fields detected | 1.69 million |

The sources characterize the gain over SAM2 as \(+88.5\%\) in mAP@0.5 and \(+103\%\) in mAP@[0.5:0.95], with inference approximately \(400\times\) faster than SAM2 in one summary and approximately \(415\times\) faster in another, reflecting the different exact latency comparisons reported in the two supplied descriptions [2511.13417] [2504.02534]. For the smallholder size range \(0.25\)–\(1\) ha, DelAnyFlow is reported to detect up to 70% more fields than Sinergise and over 200% more than NASA Harvest, while in the \(1\)–\(10\) ha range DelAnyFlow and Sinergise perform comparably [2511.13417].

The deployment example for Ukraine states that processing 603,000 km² using 5 m Sentinel-2 composites on a single AMD Ryzen 9 9900X + RTX 5070 Ti workstation with 64 GB RAM took 5.4 hours end-to-end, excluding data download [2511.13417]. Another description states that a complete field boundary layer for Ukraine was generated in under six hours on a single workstation [2511.13417]. These values are consistent at the scale of the stated deployment.

## 7. Limitations, interpretations, and relation among the variants

The field-domain literature states several limitations directly. FBIS-22M is Europe-centric, and African and Southeast Asian smallholder systems are under-represented [2511.13417]. Single-date optical reliance can induce spurious boundaries under clouds and phenology changes, and integration of SAR and temporal composites is identified as a priority [2511.13417]. Over-aggregation is reported in sparsely bounded areas, where some very large fields larger than 1,000 ha are incorrectly merged; adjusting merging thresholds by agro-ecological zone is suggested as a possible mitigation [2511.13417].

The optical-flow literature, by contrast, frames the central limitation as fragmentation or vulnerability at motion boundaries and under occlusion when models rely on local photometric cues without object-level information [2307.16586] [2405.02608]. The SAM-enhanced FlowFormer formulation addresses this through frozen global object-aware features plus task-specific adaptation [2307.16586], whereas the ARFlow-based unsupervised formulation addresses it through semantic augmentation, homography-based regional smoothness, and optional mask-feature aggregation [2405.02608]. These are conceptually related responses to the same failure mode, but they remain architecturally distinct.

Across all usages, a recurring pattern is the combination of a broad delineation prior with task-specific structure. In the UCD-derived notation, the prior is the unified syntax of control flow, data flow, boundaries, and timelines [1610.02374]. In the optical-flow variants, the prior is SAM-derived object integrity combined with flow-specific decoding or losses [2307.16586] [2405.02608]. In field delineation, the prior is DelAny’s large-scale, multi-resolution instance segmentation combined with structured geometric post-processing and vector topology enforcement [2511.13417] [2504.02534].

This suggests that “DelAnyFlow” is best treated as a family resemblance term. The common theme is delineation of coherent entities—process structure, object motion, or agricultural parcels—using a pipeline that couples global structural cues with domain-specific mechanisms for consistency, adaptation, and final representation.

Source: https://www.emergentmind.com/topics/delineate-anything-flow-delanyflow